Overview

Dataset statistics

Number of variables105
Number of observations201
Missing cells662
Missing cells (%)3.1%
Duplicate rows1
Duplicate rows (%)0.5%
Total size in memory174.6 KiB
Average record size in memory889.3 B

Variable types

DateTime1
Unsupported7
Numeric33
Categorical64

Alerts

Chiton sp. has constant value "0.0"Constant
Pollicipes pollicipes has constant value "0.0"Constant
Aglaophenia pluma (hidrozoário branco ramificado) has constant value "0.0"Constant
Alga branca ramificada has constant value "0.0"Constant
Cerianthidae has constant value "0.0"Constant
Anémona N.I. has constant value "0.0"Constant
Actinia fragacea has constant value "0.0"Constant
Gymnangium montagui has constant value "0"Constant
Actinothoe sphyrodeta (anémona branca e laranja) has constant value "0.0"Constant
Oncidiella celtica (lesma) has constant value "0.0"Constant
Calliostoma sp. (burrié bicudo) has constant value "0.0"Constant
Alga vermelha vesicular has constant value "0.0"Constant
Alga vermelha "encaracolada" (que parece ceramium pequeno) has constant value "0.0"Constant
Ahnfeltiopsis devoniensis has constant value "0.0"Constant
Alga verde carnuda ramificada has constant value "0.0"Constant
alga verde (tipo couve flor) has constant value "0.0"Constant
Alga verde n.i. has constant value "0.0"Constant
Codium adhaerens (tipo nenúfar) has constant value "0.0"Constant
Alga castanha laminada has constant value "0.0"Constant
Alga castanha incrustante has constant value "0.0"Constant
Alga castanha carnuda has constant value "0.0"Constant
Alga castanha tufosa has constant value "0.0"Constant
Alga verde/azul - lavanda has constant value "0.0"Constant
Laminaria sp. has constant value "0.0"Constant
Coluna1 has constant value "0.0"Constant
Coluna2 has constant value "0.0"Constant
Dataset has 1 (0.5%) duplicate rowsDuplicates
Tide is highly overall correlated with Alga castanha sp.High correlation
Gibbula sp. is highly overall correlated with Nassariidae (búzio) and 2 other fieldsHigh correlation
Monodonta lineata is highly overall correlated with Didemnum sp. (ascídeas brancas) and 2 other fieldsHigh correlation
Actinia equina is highly overall correlated with Anémona branca com estrias (fam. Cerianthidae) and 2 other fieldsHigh correlation
Anemonia sulcata is highly overall correlated with Watersipora subtorquata (Ascídea)High correlation
Paracentrotus lividus is highly overall correlated with Watersipora subtorquata (Ascídea) and 1 other fieldsHigh correlation
Hymeniacidon sanguinea is highly overall correlated with Coralina elongataHigh correlation
Verrucaria maura is highly overall correlated with Watersipora subtorquata (Ascídea)High correlation
Lichina pygmaea (líquene folhoso) is highly overall correlated with Litophylum tortuosumHigh correlation
Coralina elongata is highly overall correlated with Hymeniacidon sanguinea and 5 other fieldsHigh correlation
Lithophillum incrustans is highly overall correlated with Coralina elongata and 2 other fieldsHigh correlation
Gelidium sp. (tipo raquete) is highly overall correlated with Didemnum sp. (ascídeas brancas)High correlation
Asparagopsis armata (tufosa) is highly overall correlated with Polysyncraton sp. (ascídias vermelhas) and 1 other fieldsHigh correlation
Asparagopsis armata (adulta) is highly overall correlated with Substrate and 1 other fieldsHigh correlation
Ceramium (forma taças) is highly overall correlated with Alga vermelha carnuda and 1 other fieldsHigh correlation
Mastocarpus sp. (alga preta só mediolitoral) is highly overall correlated with Codium sp. (alga verde carnuda)High correlation
Plocamium sp. is highly overall correlated with Watersipora subtorquata (Ascídea) and 5 other fieldsHigh correlation
Condracanthus is highly overall correlated with Polysyncraton sp. (ascídias vermelhas)High correlation
Alga vermelha laminada is highly overall correlated with Watersipora subtorquata (Ascídea) and 1 other fieldsHigh correlation
Hildenbrandia sp. (incrustante vermelha) is highly overall correlated with alga verde tufosa and 1 other fieldsHigh correlation
Stypocaulon scoparium (alga verde tufosa que parece pinheiro) is highly overall correlated with Osmundea pinnatifida and 1 other fieldsHigh correlation
Ulva rigida is highly overall correlated with Coralina elongata and 2 other fieldsHigh correlation
Cladophora sp. (limo) is highly overall correlated with Ophiothrix sp.High correlation
Limo N.I. is highly overall correlated with Didemnum sp. (ascídeas brancas) and 1 other fieldsHigh correlation
Ulva intestinalis is highly overall correlated with Porphyra sp.High correlation
Sphacelaria rigidula (pompons castanhos) is highly overall correlated with Sabellaria alveolata (tubos) and 1 other fieldsHigh correlation
TOTAL2 is highly overall correlated with Coralina elongata and 1 other fieldsHigh correlation
Sampler is highly overall correlated with Dictyota dichotoma (alga azul laminada)High correlation
Supratidal/Middle Intertidal is highly overall correlated with Coralina elongataHigh correlation
Substrate is highly overall correlated with Asparagopsis armata (adulta) and 2 other fieldsHigh correlation
Nassariidae (búzio) is highly overall correlated with Gibbula sp. and 4 other fieldsHigh correlation
Ophiothrix sp. is highly overall correlated with Cladophora sp. (limo) and 2 other fieldsHigh correlation
Sabellaria alveolata (tubos) is highly overall correlated with Ulva rigida and 1 other fieldsHigh correlation
Anémona branca com estrias (fam. Cerianthidae) is highly overall correlated with Gibbula sp. and 2 other fieldsHigh correlation
Polysyncraton sp. (ascídias vermelhas) is highly overall correlated with Asparagopsis armata (tufosa) and 2 other fieldsHigh correlation
Didemnum sp. (ascídeas brancas) is highly overall correlated with Gibbula sp. and 6 other fieldsHigh correlation
Burrié negro N.I. is highly overall correlated with Watersipora subtorquata (Ascídea)High correlation
Búzio/burrié n.i. is highly overall correlated with Watersipora subtorquata (Ascídea)High correlation
Watersipora subtorquata (Ascídea) is highly overall correlated with Anemonia sulcata and 34 other fieldsHigh correlation
Litophylum lichenoides is highly overall correlated with Watersipora subtorquata (Ascídea)High correlation
Litophylum tortuosum is highly overall correlated with Monodonta lineata and 5 other fieldsHigh correlation
Mesophylum lichenoides is highly overall correlated with Paracentrotus lividus and 1 other fieldsHigh correlation
Porphyra sp. is highly overall correlated with Plocamium sp. and 4 other fieldsHigh correlation
Alga vermelha filamentosa is highly overall correlated with Watersipora subtorquata (Ascídea)High correlation
Alga vermelha carnuda is highly overall correlated with Ceramium (forma taças) and 2 other fieldsHigh correlation
Chondria coerulescens (alga verde/azul filamentosa) is highly overall correlated with Ulva sp. and 1 other fieldsHigh correlation
Alga vermelha semelhante a Ulva rigida is highly overall correlated with Watersipora subtorquata (Ascídea)High correlation
Osmundea pinnatifida is highly overall correlated with Stypocaulon scoparium (alga verde tufosa que parece pinheiro) and 2 other fieldsHigh correlation
Alga vermelha ramificada (Polysiphonia?) is highly overall correlated with Asparagopsis armata (tufosa) and 3 other fieldsHigh correlation
Ulva sp. is highly overall correlated with Plocamium sp. and 3 other fieldsHigh correlation
Codium sp. (alga verde carnuda) is highly overall correlated with Mastocarpus sp. (alga preta só mediolitoral) and 1 other fieldsHigh correlation
Nemoderma sp.(alga tipo musgo) is highly overall correlated with Watersipora subtorquata (Ascídea)High correlation
alga verde tufosa is highly overall correlated with Monodonta lineata and 3 other fieldsHigh correlation
Alga verde incrustante is highly overall correlated with Watersipora subtorquata (Ascídea)High correlation
Alga verde filamentosa is highly overall correlated with Hildenbrandia sp. (incrustante vermelha) and 1 other fieldsHigh correlation
Alga amarela tipo codium viscoso is highly overall correlated with Plocamium sp. and 3 other fieldsHigh correlation
Cladostephus spongiosus (codium que parece que tem areia) is highly overall correlated with Sphacelaria rigidula (pompons castanhos) and 1 other fieldsHigh correlation
Fucus vesiculosus is highly overall correlated with Watersipora subtorquata (Ascídea)High correlation
Dictyota dichotoma (alga azul laminada) is highly overall correlated with SamplerHigh correlation
Dictyota castanha is highly overall correlated with Plocamium sp. and 1 other fieldsHigh correlation
Alga castanha sp. is highly overall correlated with Tide and 1 other fieldsHigh correlation
Alga castanha filamentosa is highly overall correlated with Actinia equina and 1 other fieldsHigh correlation
Nassariidae (búzio) is highly imbalanced (87.8%)Imbalance
Ophiothrix sp. is highly imbalanced (95.5%)Imbalance
Sabellaria alveolata (tubos) is highly imbalanced (95.5%)Imbalance
Anémona branca com estrias (fam. Cerianthidae) is highly imbalanced (93.2%)Imbalance
Polysyncraton sp. (ascídias vermelhas) is highly imbalanced (95.5%)Imbalance
Didemnum sp. (ascídeas brancas) is highly imbalanced (95.5%)Imbalance
Burrié negro N.I. is highly imbalanced (86.6%)Imbalance
Búzio/burrié n.i. is highly imbalanced (95.5%)Imbalance
Watersipora subtorquata (Ascídea) is highly imbalanced (80.9%)Imbalance
Litophylum lichenoides is highly imbalanced (95.5%)Imbalance
Litophylum tortuosum is highly imbalanced (93.2%)Imbalance
Mesophylum lichenoides is highly imbalanced (89.7%)Imbalance
Porphyra sp. is highly imbalanced (93.2%)Imbalance
Alga vermelha filamentosa is highly imbalanced (92.0%)Imbalance
Alga vermelha carnuda is highly imbalanced (95.5%)Imbalance
Chondria coerulescens (alga verde/azul filamentosa) is highly imbalanced (85.3%)Imbalance
Alga vermelha semelhante a Ulva rigida is highly imbalanced (94.3%)Imbalance
Porphira is highly imbalanced (94.3%)Imbalance
Osmundea pinnatifida is highly imbalanced (95.5%)Imbalance
Alga vermelha ramificada (Polysiphonia?) is highly imbalanced (95.5%)Imbalance
Ulva sp. is highly imbalanced (95.5%)Imbalance
Codium sp. (alga verde carnuda) is highly imbalanced (94.3%)Imbalance
Nemoderma sp.(alga tipo musgo) is highly imbalanced (95.5%)Imbalance
alga verde tufosa is highly imbalanced (95.5%)Imbalance
Alga verde incrustante is highly imbalanced (95.5%)Imbalance
Alga verde filamentosa is highly imbalanced (92.1%)Imbalance
Alga amarela tipo codium viscoso is highly imbalanced (95.5%)Imbalance
Cladostephus spongiosus (codium que parece que tem areia) is highly imbalanced (92.1%)Imbalance
Fucus vesiculosus is highly imbalanced (95.5%)Imbalance
Dictyota dichotoma (alga azul laminada) is highly imbalanced (92.1%)Imbalance
Dictyota castanha is highly imbalanced (91.5%)Imbalance
Alga castanha sp. is highly imbalanced (95.5%)Imbalance
Alga castanha filamentosa is highly imbalanced (91.5%)Imbalance
Watersipora subtorquata (Ascídea) has 167 (83.1%) missing valuesMissing
observações has 161 (80.1%) missing valuesMissing
Coluna1 has 167 (83.1%) missing valuesMissing
Coluna2 has 167 (83.1%) missing valuesMissing
Hour is an unsupported type, check if it needs cleaning or further analysisUnsupported
Water temperature (ºC) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Chthamalus sp. is an unsupported type, check if it needs cleaning or further analysisUnsupported
Balanus perforatus is an unsupported type, check if it needs cleaning or further analysisUnsupported
Patella sp. is an unsupported type, check if it needs cleaning or further analysisUnsupported
Cystoseira sp. is an unsupported type, check if it needs cleaning or further analysisUnsupported
observações is an unsupported type, check if it needs cleaning or further analysisUnsupported
Siphonaria algesirae has 149 (74.1%) zerosZeros
Gibbula sp. has 127 (63.2%) zerosZeros
Monodonta lineata has 184 (91.5%) zerosZeros
Littorina neritoides has 171 (85.1%) zerosZeros
Mytillus sp. has 144 (71.6%) zerosZeros
Actinia equina has 171 (85.1%) zerosZeros
Anemonia sulcata has 194 (96.5%) zerosZeros
Paracentrotus lividus has 180 (89.6%) zerosZeros
Hymeniacidon sanguinea has 152 (75.6%) zerosZeros
Verrucaria maura has 193 (96.0%) zerosZeros
Lichina pygmaea (líquene folhoso) has 196 (97.5%) zerosZeros
Coralina elongata has 91 (45.3%) zerosZeros
Lithophillum incrustans has 121 (60.2%) zerosZeros
Gelidium sp. (tipo raquete) has 158 (78.6%) zerosZeros
Asparagopsis armata (tufosa) has 192 (95.5%) zerosZeros
Asparagopsis armata (adulta) has 184 (91.5%) zerosZeros
Ceramium (forma taças) has 168 (83.6%) zerosZeros
Mastocarpus sp. (alga preta só mediolitoral) has 171 (85.1%) zerosZeros
Gigartina sp. (língua do diabo) has 180 (89.6%) zerosZeros
Plocamium sp. has 193 (96.0%) zerosZeros
Caulacanthus sp. has 131 (65.2%) zerosZeros
Condracanthus has 176 (87.6%) zerosZeros
Alga vermelha laminada has 194 (96.5%) zerosZeros
Hildenbrandia sp. (incrustante vermelha) has 189 (94.0%) zerosZeros
Stypocaulon scoparium (alga verde tufosa que parece pinheiro) has 193 (96.0%) zerosZeros
Ulva rigida has 117 (58.2%) zerosZeros
Cladophora sp. (limo) has 128 (63.7%) zerosZeros
Limo N.I. has 185 (92.0%) zerosZeros
Ulva intestinalis has 188 (93.5%) zerosZeros
Colpomenia sinuosa (alga bolhas) has 163 (81.1%) zerosZeros
Sphacelaria rigidula (pompons castanhos) has 182 (90.5%) zerosZeros
TOTAL2 has 32 (15.9%) zerosZeros

Reproduction

Analysis started2023-05-15 16:45:38.282839
Analysis finished2023-05-15 16:47:12.448065
Duration1 minute and 34.17 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Date
Date

Distinct132
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Minimum2012-01-25 00:00:00
Maximum2020-07-06 00:00:00
2023-05-15T17:47:12.504791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:47:12.614197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Hour
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size11.2 KiB

Tide
Real number (ℝ)

Distinct30
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75338308
Minimum0.3
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:12.707267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.63
median0.8
Q30.9
95-th percentile1
Maximum1.4
Range1.1
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.17775404
Coefficient of variation (CV)0.2359411
Kurtosis0.97322727
Mean0.75338308
Median Absolute Deviation (MAD)0.1
Skewness0.30513875
Sum151.43
Variance0.031596498
MonotonicityNot monotonic
2023-05-15T17:47:12.791023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.8 38
18.9%
0.7 37
18.4%
0.9 28
13.9%
0.5 19
9.5%
0.6 17
8.5%
1 11
 
5.5%
0.4 6
 
3.0%
1.1 3
 
1.5%
0.96 3
 
1.5%
0.92 3
 
1.5%
Other values (20) 36
17.9%
ValueCountFrequency (%)
0.3 1
 
0.5%
0.4 6
 
3.0%
0.42 1
 
0.5%
0.5 19
9.5%
0.52 1
 
0.5%
0.56 1
 
0.5%
0.59 2
 
1.0%
0.6 17
8.5%
0.61 1
 
0.5%
0.63 2
 
1.0%
ValueCountFrequency (%)
1.4 2
 
1.0%
1.2 2
 
1.0%
1.1 3
 
1.5%
1 11
 
5.5%
0.96 3
 
1.5%
0.92 3
 
1.5%
0.9 28
13.9%
0.87 2
 
1.0%
0.86 2
 
1.0%
0.85 3
 
1.5%

Weather Condition
Categorical

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Clear sky
137 
Cloudy
44 
Sunny
 
10
Rain
 
8
Fairly Cloudy
 
2

Length

Max length13
Median length9
Mean length7.9850746
Min length4

Characters and Unicode

Total characters1605
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear sky
2nd rowClear sky
3rd rowCloudy
4th rowClear sky
5th rowRain

Common Values

ValueCountFrequency (%)
Clear sky 137
68.2%
Cloudy 44
 
21.9%
Sunny 10
 
5.0%
Rain 8
 
4.0%
Fairly Cloudy 2
 
1.0%

Length

2023-05-15T17:47:12.874109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:12.966328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
clear 137
40.3%
sky 137
40.3%
cloudy 46
 
13.5%
sunny 10
 
2.9%
rain 8
 
2.4%
fairly 2
 
0.6%

Most occurring characters

ValueCountFrequency (%)
y 195
12.1%
l 185
11.5%
C 183
11.4%
a 147
9.2%
r 139
8.7%
139
8.7%
s 137
8.5%
k 137
8.5%
e 137
8.5%
u 56
 
3.5%
Other values (7) 150
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1263
78.7%
Uppercase Letter 203
 
12.6%
Space Separator 139
 
8.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 195
15.4%
l 185
14.6%
a 147
11.6%
r 139
11.0%
s 137
10.8%
k 137
10.8%
e 137
10.8%
u 56
 
4.4%
o 46
 
3.6%
d 46
 
3.6%
Other values (2) 38
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
C 183
90.1%
S 10
 
4.9%
R 8
 
3.9%
F 2
 
1.0%
Space Separator
ValueCountFrequency (%)
139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1466
91.3%
Common 139
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
y 195
13.3%
l 185
12.6%
C 183
12.5%
a 147
10.0%
r 139
9.5%
s 137
9.3%
k 137
9.3%
e 137
9.3%
u 56
 
3.8%
o 46
 
3.1%
Other values (6) 104
7.1%
Common
ValueCountFrequency (%)
139
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1605
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y 195
12.1%
l 185
11.5%
C 183
11.4%
a 147
9.2%
r 139
8.7%
139
8.7%
s 137
8.5%
k 137
8.5%
e 137
8.5%
u 56
 
3.5%
Other values (7) 150
9.3%

Water temperature (ºC)
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size11.2 KiB

Sampler
Categorical

Distinct13
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
AF SF
114 
AF AR
20 
AR SF
15 
AC AF
13 
AC AR IR
 
10
Other values (8)
29 

Length

Max length8
Median length5
Mean length5.3134328
Min length5

Characters and Unicode

Total characters1068
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowAC AR
2nd rowAF SF
3rd rowAF AR SF
4th rowAF SF
5th rowAC AR

Common Values

ValueCountFrequency (%)
AF SF 114
56.7%
AF AR 20
 
10.0%
AR SF 15
 
7.5%
AC AF 13
 
6.5%
AC AR IR 10
 
5.0%
AC AR 9
 
4.5%
AC SF 5
 
2.5%
AF AR SF 4
 
2.0%
AC AF IR 3
 
1.5%
AF NA 3
 
1.5%
Other values (3) 5
 
2.5%

Length

2023-05-15T17:47:13.047879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
af 160
37.8%
sf 142
33.6%
ar 59
 
13.9%
ac 44
 
10.4%
ir 15
 
3.5%
na 3
 
0.7%

Most occurring characters

ValueCountFrequency (%)
F 302
28.3%
A 266
24.9%
222
20.8%
S 142
13.3%
R 74
 
6.9%
C 44
 
4.1%
I 15
 
1.4%
N 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 846
79.2%
Space Separator 222
 
20.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 302
35.7%
A 266
31.4%
S 142
16.8%
R 74
 
8.7%
C 44
 
5.2%
I 15
 
1.8%
N 3
 
0.4%
Space Separator
ValueCountFrequency (%)
222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 846
79.2%
Common 222
 
20.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 302
35.7%
A 266
31.4%
S 142
16.8%
R 74
 
8.7%
C 44
 
5.2%
I 15
 
1.8%
N 3
 
0.4%
Common
ValueCountFrequency (%)
222
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 302
28.3%
A 266
24.9%
222
20.8%
S 142
13.3%
R 74
 
6.9%
C 44
 
4.1%
I 15
 
1.4%
N 3
 
0.3%

Zone
Categorical

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
A
62 
D
50 
E
43 
B
42 
F
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters201
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowD
4th rowA
5th rowD

Common Values

ValueCountFrequency (%)
A 62
30.8%
D 50
24.9%
E 43
21.4%
B 42
20.9%
F 4
 
2.0%

Length

2023-05-15T17:47:13.129093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:13.217678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
a 62
30.8%
d 50
24.9%
e 43
21.4%
b 42
20.9%
f 4
 
2.0%

Most occurring characters

ValueCountFrequency (%)
A 62
30.8%
D 50
24.9%
E 43
21.4%
B 42
20.9%
F 4
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 201
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 62
30.8%
D 50
24.9%
E 43
21.4%
B 42
20.9%
F 4
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 201
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 62
30.8%
D 50
24.9%
E 43
21.4%
B 42
20.9%
F 4
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 62
30.8%
D 50
24.9%
E 43
21.4%
B 42
20.9%
F 4
 
2.0%

Supratidal/Middle Intertidal
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Medium
124 
Supra
77 

Length

Max length6
Median length6
Mean length5.6169154
Min length5

Characters and Unicode

Total characters1129
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSupra
2nd rowMedium
3rd rowMedium
4th rowSupra
5th rowSupra

Common Values

ValueCountFrequency (%)
Medium 124
61.7%
Supra 77
38.3%

Length

2023-05-15T17:47:13.300599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:13.386739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
medium 124
61.7%
supra 77
38.3%

Most occurring characters

ValueCountFrequency (%)
u 201
17.8%
M 124
11.0%
e 124
11.0%
d 124
11.0%
i 124
11.0%
m 124
11.0%
S 77
 
6.8%
p 77
 
6.8%
r 77
 
6.8%
a 77
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 928
82.2%
Uppercase Letter 201
 
17.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 201
21.7%
e 124
13.4%
d 124
13.4%
i 124
13.4%
m 124
13.4%
p 77
 
8.3%
r 77
 
8.3%
a 77
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
M 124
61.7%
S 77
38.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1129
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 201
17.8%
M 124
11.0%
e 124
11.0%
d 124
11.0%
i 124
11.0%
m 124
11.0%
S 77
 
6.8%
p 77
 
6.8%
r 77
 
6.8%
a 77
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 201
17.8%
M 124
11.0%
e 124
11.0%
d 124
11.0%
i 124
11.0%
m 124
11.0%
S 77
 
6.8%
p 77
 
6.8%
r 77
 
6.8%
a 77
 
6.8%

Substrate
Categorical

Distinct14
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Rock
76 
Rock/Sand
53 
Puddle/Rock
31 
Sand
9 
Puddle/Rock/Sand
9 
Other values (9)
23 

Length

Max length16
Median length13
Mean length7.5273632
Min length4

Characters and Unicode

Total characters1513
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)2.0%

Sample

1st rowRock
2nd rowPuddle/Rock
3rd rowRock
4th rowSand/Pebble
5th rowRock

Common Values

ValueCountFrequency (%)
Rock 76
37.8%
Rock/Sand 53
26.4%
Puddle/Rock 31
15.4%
Sand 9
 
4.5%
Puddle/Rock/Sand 9
 
4.5%
Pebble 8
 
4.0%
Sand/Pebble 3
 
1.5%
Rock/Sand/Pebble 3
 
1.5%
Puddle 3
 
1.5%
Rock/Canal 2
 
1.0%
Other values (4) 4
 
2.0%

Length

2023-05-15T17:47:13.461603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rock 76
37.6%
rock/sand 53
26.2%
puddle/rock 31
15.3%
sand 9
 
4.5%
puddle/rock/sand 9
 
4.5%
pebble 8
 
4.0%
sand/pebble 3
 
1.5%
rock/sand/pebble 3
 
1.5%
puddle 3
 
1.5%
rock/canal 2
 
1.0%
Other values (5) 5
 
2.5%

Most occurring characters

ValueCountFrequency (%)
R 176
11.6%
c 176
11.6%
k 176
11.6%
o 176
11.6%
d 168
11.1%
/ 117
7.7%
a 84
5.6%
n 81
 
5.4%
S 80
 
5.3%
e 76
 
5.0%
Other values (11) 203
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1076
71.1%
Uppercase Letter 319
 
21.1%
Other Punctuation 117
 
7.7%
Space Separator 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 176
16.4%
k 176
16.4%
o 176
16.4%
d 168
15.6%
a 84
7.8%
n 81
7.5%
e 76
7.1%
l 61
 
5.7%
u 44
 
4.1%
b 30
 
2.8%
Other values (3) 4
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
R 176
55.2%
S 80
25.1%
P 59
 
18.5%
C 2
 
0.6%
H 1
 
0.3%
T 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 117
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1395
92.2%
Common 118
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 176
12.6%
c 176
12.6%
k 176
12.6%
o 176
12.6%
d 168
12.0%
a 84
6.0%
n 81
5.8%
S 80
5.7%
e 76
5.4%
l 61
 
4.4%
Other values (9) 141
10.1%
Common
ValueCountFrequency (%)
/ 117
99.2%
1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 176
11.6%
c 176
11.6%
k 176
11.6%
o 176
11.6%
d 168
11.1%
/ 117
7.7%
a 84
5.6%
n 81
 
5.4%
S 80
 
5.3%
e 76
 
5.0%
Other values (11) 203
13.4%

Chthamalus sp.
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size11.2 KiB

Balanus perforatus
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size11.2 KiB

Patella sp.
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size11.2 KiB

Siphonaria algesirae
Real number (ℝ)

Distinct16
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23880597
Minimum0
Maximum7
Zeros149
Zeros (%)74.1%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:13.539350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile1.2
Maximum7
Range7
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.86306811
Coefficient of variation (CV)3.6140977
Kurtosis36.448961
Mean0.23880597
Median Absolute Deviation (MAD)0
Skewness5.7067946
Sum48
Variance0.74488657
MonotonicityNot monotonic
2023-05-15T17:47:13.613419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 149
74.1%
0.1 13
 
6.5%
0.2 9
 
4.5%
0.3 7
 
3.5%
2 5
 
2.5%
0.6 4
 
2.0%
0.7 3
 
1.5%
0.4 2
 
1.0%
0.5 2
 
1.0%
1.2 1
 
0.5%
Other values (6) 6
 
3.0%
ValueCountFrequency (%)
0 149
74.1%
0.1 13
 
6.5%
0.2 9
 
4.5%
0.3 7
 
3.5%
0.4 2
 
1.0%
0.5 2
 
1.0%
0.6 4
 
2.0%
0.7 3
 
1.5%
1.1 1
 
0.5%
1.2 1
 
0.5%
ValueCountFrequency (%)
7 1
 
0.5%
6.2 1
 
0.5%
5.5 1
 
0.5%
3 1
 
0.5%
2.5 1
 
0.5%
2 5
2.5%
1.2 1
 
0.5%
1.1 1
 
0.5%
0.7 3
1.5%
0.6 4
2.0%

Gibbula sp.
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29800995
Minimum0
Maximum10
Zeros127
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:13.706354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile1.1
Maximum10
Range10
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.98508681
Coefficient of variation (CV)3.3055501
Kurtosis54.228299
Mean0.29800995
Median Absolute Deviation (MAD)0
Skewness6.6830082
Sum59.9
Variance0.97039602
MonotonicityNot monotonic
2023-05-15T17:47:13.800535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 127
63.2%
0.2 16
 
8.0%
0.1 15
 
7.5%
0.6 7
 
3.5%
0.3 7
 
3.5%
0.5 6
 
3.0%
0.4 4
 
2.0%
0.7 3
 
1.5%
1 3
 
1.5%
1.1 2
 
1.0%
Other values (10) 11
 
5.5%
ValueCountFrequency (%)
0 127
63.2%
0.1 15
 
7.5%
0.2 16
 
8.0%
0.3 7
 
3.5%
0.4 4
 
2.0%
0.5 6
 
3.0%
0.6 7
 
3.5%
0.7 3
 
1.5%
0.8 1
 
0.5%
1 3
 
1.5%
ValueCountFrequency (%)
10 1
0.5%
5.2 1
0.5%
5 2
1.0%
2.5 1
0.5%
2.2 1
0.5%
1.9 1
0.5%
1.7 1
0.5%
1.5 1
0.5%
1.2 1
0.5%
1.1 2
1.0%

Monodonta lineata
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075124378
Minimum0
Maximum3.2
Zeros184
Zeros (%)91.5%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:13.888234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.2
Maximum3.2
Range3.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3742701
Coefficient of variation (CV)4.982006
Kurtosis46.647234
Mean0.075124378
Median Absolute Deviation (MAD)0
Skewness6.5592241
Sum15.1
Variance0.14007811
MonotonicityNot monotonic
2023-05-15T17:47:13.965481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 184
91.5%
0.2 5
 
2.5%
0.4 2
 
1.0%
0.1 2
 
1.0%
0.6 1
 
0.5%
0.9 1
 
0.5%
1 1
 
0.5%
0.8 1
 
0.5%
3 1
 
0.5%
2 1
 
0.5%
Other values (2) 2
 
1.0%
ValueCountFrequency (%)
0 184
91.5%
0.1 2
 
1.0%
0.2 5
 
2.5%
0.4 2
 
1.0%
0.6 1
 
0.5%
0.8 1
 
0.5%
0.9 1
 
0.5%
1 1
 
0.5%
1.6 1
 
0.5%
2 1
 
0.5%
ValueCountFrequency (%)
3.2 1
 
0.5%
3 1
 
0.5%
2 1
 
0.5%
1.6 1
 
0.5%
1 1
 
0.5%
0.9 1
 
0.5%
0.8 1
 
0.5%
0.6 1
 
0.5%
0.4 2
 
1.0%
0.2 5
2.5%

Littorina neritoides
Real number (ℝ)

Distinct10
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20104478
Minimum0
Maximum12
Zeros171
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:14.045925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.97374504
Coefficient of variation (CV)4.8434237
Kurtosis109.39217
Mean0.20104478
Median Absolute Deviation (MAD)0
Skewness9.5109967
Sum40.41
Variance0.9481794
MonotonicityNot monotonic
2023-05-15T17:47:14.122381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 171
85.1%
0.5 6
 
3.0%
2 5
 
2.5%
0.1 5
 
2.5%
1 5
 
2.5%
0.2 3
 
1.5%
3 3
 
1.5%
12 1
 
0.5%
0.01 1
 
0.5%
0.3 1
 
0.5%
ValueCountFrequency (%)
0 171
85.1%
0.01 1
 
0.5%
0.1 5
 
2.5%
0.2 3
 
1.5%
0.3 1
 
0.5%
0.5 6
 
3.0%
1 5
 
2.5%
2 5
 
2.5%
3 3
 
1.5%
12 1
 
0.5%
ValueCountFrequency (%)
12 1
 
0.5%
3 3
 
1.5%
2 5
 
2.5%
1 5
 
2.5%
0.5 6
 
3.0%
0.3 1
 
0.5%
0.2 3
 
1.5%
0.1 5
 
2.5%
0.01 1
 
0.5%
0 171
85.1%

Mytillus sp.
Real number (ℝ)

Distinct22
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3800995
Minimum0
Maximum66
Zeros144
Zeros (%)71.6%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:14.197967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile7
Maximum66
Range66
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation5.6146239
Coefficient of variation (CV)4.0682747
Kurtosis91.065538
Mean1.3800995
Median Absolute Deviation (MAD)0
Skewness8.6108687
Sum277.4
Variance31.524002
MonotonicityNot monotonic
2023-05-15T17:47:14.291447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 144
71.6%
1 13
 
6.5%
3 6
 
3.0%
0.1 5
 
2.5%
0.5 4
 
2.0%
2 4
 
2.0%
0.2 4
 
2.0%
6 4
 
2.0%
5 2
 
1.0%
7 2
 
1.0%
Other values (12) 13
 
6.5%
ValueCountFrequency (%)
0 144
71.6%
0.1 5
 
2.5%
0.2 4
 
2.0%
0.3 2
 
1.0%
0.5 4
 
2.0%
1 13
 
6.5%
1.5 1
 
0.5%
2 4
 
2.0%
3 6
 
3.0%
4 1
 
0.5%
ValueCountFrequency (%)
66 1
0.5%
29 1
0.5%
16 1
0.5%
15 1
0.5%
13 1
0.5%
12 1
0.5%
11 1
0.5%
10 1
0.5%
9 1
0.5%
7 2
1.0%

Nassariidae (búzio)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
194 
0.1
 
3
0.2
 
2
0.3
 
1
2.4
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 194
96.5%
0.1 3
 
1.5%
0.2 2
 
1.0%
0.3 1
 
0.5%
2.4 1
 
0.5%

Length

2023-05-15T17:47:14.386284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:14.483267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 194
96.5%
0.1 3
 
1.5%
0.2 2
 
1.0%
0.3 1
 
0.5%
2.4 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 394
65.3%
. 201
33.3%
1 3
 
0.5%
2 3
 
0.5%
3 1
 
0.2%
4 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 394
98.0%
1 3
 
0.7%
2 3
 
0.7%
3 1
 
0.2%
4 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 394
65.3%
. 201
33.3%
1 3
 
0.5%
2 3
 
0.5%
3 1
 
0.2%
4 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 394
65.3%
. 201
33.3%
1 3
 
0.5%
2 3
 
0.5%
3 1
 
0.2%
4 1
 
0.2%

Chiton sp.
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:14.561373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:14.638611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Pollicipes pollicipes
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:14.701180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:14.781775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Actinia equina
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14427861
Minimum0
Maximum3.5
Zeros171
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:14.852308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum3.5
Range3.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49158377
Coefficient of variation (CV)3.4071841
Kurtosis24.294608
Mean0.14427861
Median Absolute Deviation (MAD)0
Skewness4.7015604
Sum29
Variance0.2416546
MonotonicityNot monotonic
2023-05-15T17:47:14.936093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 171
85.1%
0.5 7
 
3.5%
1 4
 
2.0%
0.75 3
 
1.5%
0.25 3
 
1.5%
3 2
 
1.0%
0.2 2
 
1.0%
3.5 1
 
0.5%
0.6 1
 
0.5%
0.4 1
 
0.5%
Other values (6) 6
 
3.0%
ValueCountFrequency (%)
0 171
85.1%
0.1 1
 
0.5%
0.2 2
 
1.0%
0.25 3
 
1.5%
0.3 1
 
0.5%
0.4 1
 
0.5%
0.5 7
 
3.5%
0.6 1
 
0.5%
0.75 3
 
1.5%
1 4
 
2.0%
ValueCountFrequency (%)
3.5 1
 
0.5%
3 2
 
1.0%
2.5 1
 
0.5%
2 1
 
0.5%
1.5 1
 
0.5%
1.2 1
 
0.5%
1 4
2.0%
0.75 3
1.5%
0.6 1
 
0.5%
0.5 7
3.5%

Anemonia sulcata
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14751244
Minimum0
Maximum18
Zeros194
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:15.023675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4496357
Coefficient of variation (CV)9.8272102
Kurtosis126.45937
Mean0.14751244
Median Absolute Deviation (MAD)0
Skewness11.017273
Sum29.65
Variance2.1014438
MonotonicityNot monotonic
2023-05-15T17:47:15.096434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 194
96.5%
0.5 2
 
1.0%
0.1 1
 
0.5%
0.3 1
 
0.5%
0.25 1
 
0.5%
10 1
 
0.5%
18 1
 
0.5%
ValueCountFrequency (%)
0 194
96.5%
0.1 1
 
0.5%
0.25 1
 
0.5%
0.3 1
 
0.5%
0.5 2
 
1.0%
10 1
 
0.5%
18 1
 
0.5%
ValueCountFrequency (%)
18 1
 
0.5%
10 1
 
0.5%
0.5 2
 
1.0%
0.3 1
 
0.5%
0.25 1
 
0.5%
0.1 1
 
0.5%
0 194
96.5%

Ophiothrix sp.
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
4.0 1
 
0.5%

Length

2023-05-15T17:47:15.179779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:15.507522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
4.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
4 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
4 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
4 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
4 1
 
0.2%

Paracentrotus lividus
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98905473
Minimum0
Maximum40
Zeros180
Zeros (%)89.6%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:15.571904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.2188363
Coefficient of variation (CV)4.2655236
Kurtosis42.297014
Mean0.98905473
Median Absolute Deviation (MAD)0
Skewness5.9123549
Sum198.8
Variance17.79858
MonotonicityNot monotonic
2023-05-15T17:47:15.653872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 180
89.6%
1 3
 
1.5%
2 3
 
1.5%
20 2
 
1.0%
5 2
 
1.0%
40 1
 
0.5%
17 1
 
0.5%
8 1
 
0.5%
16.5 1
 
0.5%
14 1
 
0.5%
Other values (6) 6
 
3.0%
ValueCountFrequency (%)
0 180
89.6%
0.3 1
 
0.5%
1 3
 
1.5%
2 3
 
1.5%
4 1
 
0.5%
5 2
 
1.0%
6 1
 
0.5%
7 1
 
0.5%
8 1
 
0.5%
12 1
 
0.5%
ValueCountFrequency (%)
40 1
0.5%
20 2
1.0%
17 1
0.5%
16.5 1
0.5%
15 1
0.5%
14 1
0.5%
12 1
0.5%
8 1
0.5%
7 1
0.5%
6 1
0.5%

Hymeniacidon sanguinea
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16840796
Minimum0
Maximum6
Zeros152
Zeros (%)75.6%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:15.735720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61661127
Coefficient of variation (CV)3.661414
Kurtosis58.384159
Mean0.16840796
Median Absolute Deviation (MAD)0
Skewness7.0396638
Sum33.85
Variance0.38020945
MonotonicityNot monotonic
2023-05-15T17:47:15.803632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 152
75.6%
0.1 16
 
8.0%
0.5 10
 
5.0%
1 9
 
4.5%
0.2 4
 
2.0%
0.3 4
 
2.0%
1.5 2
 
1.0%
0.25 1
 
0.5%
5 1
 
0.5%
2 1
 
0.5%
ValueCountFrequency (%)
0 152
75.6%
0.1 16
 
8.0%
0.2 4
 
2.0%
0.25 1
 
0.5%
0.3 4
 
2.0%
0.5 10
 
5.0%
1 9
 
4.5%
1.5 2
 
1.0%
2 1
 
0.5%
5 1
 
0.5%
ValueCountFrequency (%)
6 1
 
0.5%
5 1
 
0.5%
2 1
 
0.5%
1.5 2
 
1.0%
1 9
4.5%
0.5 10
5.0%
0.3 4
 
2.0%
0.25 1
 
0.5%
0.2 4
 
2.0%
0.1 16
8.0%

Aglaophenia pluma (hidrozoário branco ramificado)
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:15.882133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:15.961050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Alga branca ramificada
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:16.020854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:16.099867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Sabellaria alveolata (tubos)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
0.5
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
0.5 1
 
0.5%

Length

2023-05-15T17:47:16.164236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:16.245806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
0.5 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
5 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Cerianthidae
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:16.308214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:16.386235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Anémona N.I.
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:16.448917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:16.530903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Actinia fragacea
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:16.594296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:16.675046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Gymnangium montagui
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0
201 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters201
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 201
100.0%

Length

2023-05-15T17:47:16.743460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:16.820454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 201
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 201
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 201
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 201
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 201
100.0%

Anémona branca com estrias (fam. Cerianthidae)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
198 
1.0
 
1
0.25
 
1
0.1
 
1

Length

Max length4
Median length3
Mean length3.0049751
Min length3

Characters and Unicode

Total characters604
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.5%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 198
98.5%
1.0 1
 
0.5%
0.25 1
 
0.5%
0.1 1
 
0.5%

Length

2023-05-15T17:47:16.883654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:16.972910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 198
98.5%
1.0 1
 
0.5%
0.25 1
 
0.5%
0.1 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 399
66.1%
. 201
33.3%
1 2
 
0.3%
2 1
 
0.2%
5 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 403
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 399
99.0%
1 2
 
0.5%
2 1
 
0.2%
5 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 604
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 399
66.1%
. 201
33.3%
1 2
 
0.3%
2 1
 
0.2%
5 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 604
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 399
66.1%
. 201
33.3%
1 2
 
0.3%
2 1
 
0.2%
5 1
 
0.2%

Actinothoe sphyrodeta (anémona branca e laranja)
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:17.055006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:17.146542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Oncidiella celtica (lesma)
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:17.217860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:17.306851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Calliostoma sp. (burrié bicudo)
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:17.371691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:17.454528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Polysyncraton sp. (ascídias vermelhas)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
0.5
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
0.5 1
 
0.5%

Length

2023-05-15T17:47:17.518839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:17.602699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
0.5 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
5 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Didemnum sp. (ascídeas brancas)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
1.0 1
 
0.5%

Length

2023-05-15T17:47:17.667726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:17.749564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
1.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
1 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
1 1
 
0.2%

Burrié negro N.I.
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
193 
0.1
 
4
0.2
 
2
0.3
 
1
0.5
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 193
96.0%
0.1 4
 
2.0%
0.2 2
 
1.0%
0.3 1
 
0.5%
0.5 1
 
0.5%

Length

2023-05-15T17:47:17.821133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:17.922112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 193
96.0%
0.1 4
 
2.0%
0.2 2
 
1.0%
0.3 1
 
0.5%
0.5 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 394
65.3%
. 201
33.3%
1 4
 
0.7%
2 2
 
0.3%
3 1
 
0.2%
5 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 394
98.0%
1 4
 
1.0%
2 2
 
0.5%
3 1
 
0.2%
5 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 394
65.3%
. 201
33.3%
1 4
 
0.7%
2 2
 
0.3%
3 1
 
0.2%
5 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 394
65.3%
. 201
33.3%
1 4
 
0.7%
2 2
 
0.3%
3 1
 
0.2%
5 1
 
0.2%

Búzio/burrié n.i.
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
0.2
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
0.2 1
 
0.5%

Length

2023-05-15T17:47:18.006137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:18.094398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
0.2 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
2 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
2 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
2 1
 
0.2%

Watersipora subtorquata (Ascídea)
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)5.9%
Missing167
Missing (%)83.1%
Memory size11.2 KiB
0.0
33 
0.2
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters102
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 33
 
16.4%
0.2 1
 
0.5%
(Missing) 167
83.1%

Length

2023-05-15T17:47:18.161648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:18.240378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 33
97.1%
0.2 1
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 67
65.7%
. 34
33.3%
2 1
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68
66.7%
Other Punctuation 34
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 67
98.5%
2 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 67
65.7%
. 34
33.3%
2 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 67
65.7%
. 34
33.3%
2 1
 
1.0%

Verrucaria maura
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33537313
Minimum0
Maximum35
Zeros193
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:18.302631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum35
Range35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.0401255
Coefficient of variation (CV)9.0649047
Kurtosis106.38007
Mean0.33537313
Median Absolute Deviation (MAD)0
Skewness10.192126
Sum67.41
Variance9.242363
MonotonicityNot monotonic
2023-05-15T17:47:18.371180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 193
96.0%
0.2 2
 
1.0%
25 1
 
0.5%
0.01 1
 
0.5%
2 1
 
0.5%
35 1
 
0.5%
1 1
 
0.5%
4 1
 
0.5%
ValueCountFrequency (%)
0 193
96.0%
0.01 1
 
0.5%
0.2 2
 
1.0%
1 1
 
0.5%
2 1
 
0.5%
4 1
 
0.5%
25 1
 
0.5%
35 1
 
0.5%
ValueCountFrequency (%)
35 1
 
0.5%
25 1
 
0.5%
4 1
 
0.5%
2 1
 
0.5%
1 1
 
0.5%
0.2 2
 
1.0%
0.01 1
 
0.5%
0 193
96.0%

Lichina pygmaea (líquene folhoso)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19104478
Minimum0
Maximum25
Zeros196
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:18.446406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.906258
Coefficient of variation (CV)9.978069
Kurtosis148.40187
Mean0.19104478
Median Absolute Deviation (MAD)0
Skewness11.835756
Sum38.4
Variance3.6338194
MonotonicityNot monotonic
2023-05-15T17:47:18.516201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 196
97.5%
25 1
 
0.5%
0.3 1
 
0.5%
10 1
 
0.5%
3 1
 
0.5%
0.1 1
 
0.5%
ValueCountFrequency (%)
0 196
97.5%
0.1 1
 
0.5%
0.3 1
 
0.5%
3 1
 
0.5%
10 1
 
0.5%
25 1
 
0.5%
ValueCountFrequency (%)
25 1
 
0.5%
10 1
 
0.5%
3 1
 
0.5%
0.3 1
 
0.5%
0.1 1
 
0.5%
0 196
97.5%

Coralina elongata
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.394527
Minimum0
Maximum96
Zeros91
Zeros (%)45.3%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:18.610103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q325
95-th percentile60
Maximum96
Range96
Interquartile range (IQR)25

Descriptive statistics

Standard deviation21.620664
Coefficient of variation (CV)1.5020058
Kurtosis2.1324869
Mean14.394527
Median Absolute Deviation (MAD)1
Skewness1.6503337
Sum2893.3
Variance467.45312
MonotonicityNot monotonic
2023-05-15T17:47:18.715832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 91
45.3%
30 8
 
4.0%
1 7
 
3.5%
20 6
 
3.0%
3 5
 
2.5%
25 4
 
2.0%
8 4
 
2.0%
60 4
 
2.0%
35 4
 
2.0%
50 4
 
2.0%
Other values (39) 64
31.8%
ValueCountFrequency (%)
0 91
45.3%
0.1 4
 
2.0%
0.3 3
 
1.5%
1 7
 
3.5%
2 2
 
1.0%
3 5
 
2.5%
4 3
 
1.5%
5 2
 
1.0%
6 1
 
0.5%
7 3
 
1.5%
ValueCountFrequency (%)
96 1
0.5%
87 1
0.5%
85 1
0.5%
83 1
0.5%
80 1
0.5%
75 1
0.5%
70 1
0.5%
66 1
0.5%
63 1
0.5%
62 1
0.5%

Lithophillum incrustans
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9218905
Minimum0
Maximum27
Zeros121
Zeros (%)60.2%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:18.805433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10
Maximum27
Range27
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.2114034
Coefficient of variation (CV)2.1912816
Kurtosis9.9351956
Mean1.9218905
Median Absolute Deviation (MAD)0
Skewness2.9899943
Sum386.3
Variance17.735918
MonotonicityNot monotonic
2023-05-15T17:47:18.886741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 121
60.2%
1 15
 
7.5%
3 8
 
4.0%
4 7
 
3.5%
0.1 6
 
3.0%
2 6
 
3.0%
0.5 5
 
2.5%
10 5
 
2.5%
6 4
 
2.0%
5 4
 
2.0%
Other values (12) 20
 
10.0%
ValueCountFrequency (%)
0 121
60.2%
0.1 6
 
3.0%
0.2 3
 
1.5%
0.3 2
 
1.0%
0.5 5
 
2.5%
1 15
 
7.5%
2 6
 
3.0%
3 8
 
4.0%
4 7
 
3.5%
5 4
 
2.0%
ValueCountFrequency (%)
27 1
 
0.5%
19 1
 
0.5%
18 1
 
0.5%
16 3
1.5%
15 2
 
1.0%
14 1
 
0.5%
13 1
 
0.5%
10 5
2.5%
9 2
 
1.0%
8 1
 
0.5%

Litophylum lichenoides
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
3.0 1
 
0.5%

Length

2023-05-15T17:47:18.964751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:19.044842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
3.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
3 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
3 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
3 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
3 1
 
0.2%

Litophylum tortuosum
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
198 
0.5
 
1
0.2
 
1
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 198
98.5%
0.5 1
 
0.5%
0.2 1
 
0.5%
3.0 1
 
0.5%

Length

2023-05-15T17:47:19.125941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:19.226952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 198
98.5%
0.5 1
 
0.5%
0.2 1
 
0.5%
3.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
5 1
 
0.2%
2 1
 
0.2%
3 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 399
99.3%
5 1
 
0.2%
2 1
 
0.2%
3 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
5 1
 
0.2%
2 1
 
0.2%
3 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
5 1
 
0.2%
2 1
 
0.2%
3 1
 
0.2%

Mesophylum lichenoides
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
196 
0.2
 
2
0.5
 
2
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 196
97.5%
0.2 2
 
1.0%
0.5 2
 
1.0%
1.0 1
 
0.5%

Length

2023-05-15T17:47:19.309294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:19.408758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 196
97.5%
0.2 2
 
1.0%
0.5 2
 
1.0%
1.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 397
65.8%
. 201
33.3%
2 2
 
0.3%
5 2
 
0.3%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 397
98.8%
2 2
 
0.5%
5 2
 
0.5%
1 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 397
65.8%
. 201
33.3%
2 2
 
0.3%
5 2
 
0.3%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 397
65.8%
. 201
33.3%
2 2
 
0.3%
5 2
 
0.3%
1 1
 
0.2%

Gelidium sp. (tipo raquete)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63905473
Minimum0
Maximum24
Zeros158
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:19.486122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4811448
Coefficient of variation (CV)3.8825232
Kurtosis54.206991
Mean0.63905473
Median Absolute Deviation (MAD)0
Skewness6.7580262
Sum128.45
Variance6.1560796
MonotonicityNot monotonic
2023-05-15T17:47:19.564280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 158
78.6%
1 8
 
4.0%
0.5 8
 
4.0%
4 5
 
2.5%
2 4
 
2.0%
0.2 3
 
1.5%
0.1 3
 
1.5%
3 2
 
1.0%
5 2
 
1.0%
6 2
 
1.0%
Other values (6) 6
 
3.0%
ValueCountFrequency (%)
0 158
78.6%
0.1 3
 
1.5%
0.2 3
 
1.5%
0.25 1
 
0.5%
0.3 1
 
0.5%
0.5 8
 
4.0%
1 8
 
4.0%
2 4
 
2.0%
3 2
 
1.0%
4 5
 
2.5%
ValueCountFrequency (%)
24 1
 
0.5%
19 1
 
0.5%
9 1
 
0.5%
7 1
 
0.5%
6 2
 
1.0%
5 2
 
1.0%
4 5
2.5%
3 2
 
1.0%
2 4
2.0%
1 8
4.0%

Asparagopsis armata (tufosa)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23781095
Minimum0
Maximum30
Zeros192
Zeros (%)95.5%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:19.635598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1793722
Coefficient of variation (CV)9.1643057
Kurtosis176.30532
Mean0.23781095
Median Absolute Deviation (MAD)0
Skewness12.951784
Sum47.8
Variance4.7496632
MonotonicityNot monotonic
2023-05-15T17:47:19.700146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 192
95.5%
4 3
 
1.5%
0.2 1
 
0.5%
3 1
 
0.5%
0.1 1
 
0.5%
30 1
 
0.5%
0.5 1
 
0.5%
2 1
 
0.5%
ValueCountFrequency (%)
0 192
95.5%
0.1 1
 
0.5%
0.2 1
 
0.5%
0.5 1
 
0.5%
2 1
 
0.5%
3 1
 
0.5%
4 3
 
1.5%
30 1
 
0.5%
ValueCountFrequency (%)
30 1
 
0.5%
4 3
 
1.5%
3 1
 
0.5%
2 1
 
0.5%
0.5 1
 
0.5%
0.2 1
 
0.5%
0.1 1
 
0.5%
0 192
95.5%

Asparagopsis armata (adulta)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22636816
Minimum0
Maximum24
Zeros184
Zeros (%)91.5%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:19.768109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7439757
Coefficient of variation (CV)7.7041564
Kurtosis174.9669
Mean0.22636816
Median Absolute Deviation (MAD)0
Skewness12.860135
Sum45.5
Variance3.0414512
MonotonicityNot monotonic
2023-05-15T17:47:19.836551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 184
91.5%
2 5
 
2.5%
1 4
 
2.0%
0.5 2
 
1.0%
0.2 2
 
1.0%
3 2
 
1.0%
0.1 1
 
0.5%
24 1
 
0.5%
ValueCountFrequency (%)
0 184
91.5%
0.1 1
 
0.5%
0.2 2
 
1.0%
0.5 2
 
1.0%
1 4
 
2.0%
2 5
 
2.5%
3 2
 
1.0%
24 1
 
0.5%
ValueCountFrequency (%)
24 1
 
0.5%
3 2
 
1.0%
2 5
 
2.5%
1 4
 
2.0%
0.5 2
 
1.0%
0.2 2
 
1.0%
0.1 1
 
0.5%
0 184
91.5%

Porphyra sp.
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
198 
0.2
 
1
5.0
 
1
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 198
98.5%
0.2 1
 
0.5%
5.0 1
 
0.5%
1.0 1
 
0.5%

Length

2023-05-15T17:47:19.907181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:19.991817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 198
98.5%
0.2 1
 
0.5%
5.0 1
 
0.5%
1.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
2 1
 
0.2%
5 1
 
0.2%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 399
99.3%
2 1
 
0.2%
5 1
 
0.2%
1 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
2 1
 
0.2%
5 1
 
0.2%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
2 1
 
0.2%
5 1
 
0.2%
1 1
 
0.2%

Ceramium (forma taças)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4263682
Minimum0
Maximum50
Zeros168
Zeros (%)83.6%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:20.062939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.0479657
Coefficient of variation (CV)3.3168774
Kurtosis17.018296
Mean2.4263682
Median Absolute Deviation (MAD)0
Skewness4.0574752
Sum487.7
Variance64.769751
MonotonicityNot monotonic
2023-05-15T17:47:20.360142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 168
83.6%
1 4
 
2.0%
3 3
 
1.5%
12 2
 
1.0%
30 2
 
1.0%
5 2
 
1.0%
6 2
 
1.0%
10 2
 
1.0%
15 2
 
1.0%
50 1
 
0.5%
Other values (13) 13
 
6.5%
ValueCountFrequency (%)
0 168
83.6%
0.2 1
 
0.5%
0.5 1
 
0.5%
1 4
 
2.0%
2 1
 
0.5%
3 3
 
1.5%
5 2
 
1.0%
6 2
 
1.0%
7 1
 
0.5%
10 2
 
1.0%
ValueCountFrequency (%)
50 1
0.5%
46 1
0.5%
43 1
0.5%
40 1
0.5%
35 1
0.5%
30 2
1.0%
25 1
0.5%
23 1
0.5%
18 1
0.5%
16 1
0.5%

Mastocarpus sp. (alga preta só mediolitoral)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5880597
Minimum0
Maximum18
Zeros171
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:20.430274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.264433
Coefficient of variation (CV)3.8506855
Kurtosis28.204612
Mean0.5880597
Median Absolute Deviation (MAD)0
Skewness5.0864286
Sum118.2
Variance5.1276567
MonotonicityNot monotonic
2023-05-15T17:47:20.497244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 171
85.1%
1 10
 
5.0%
0.5 4
 
2.0%
2 3
 
1.5%
8 2
 
1.0%
3 2
 
1.0%
4 2
 
1.0%
6 1
 
0.5%
0.2 1
 
0.5%
10 1
 
0.5%
Other values (4) 4
 
2.0%
ValueCountFrequency (%)
0 171
85.1%
0.2 1
 
0.5%
0.5 4
 
2.0%
1 10
 
5.0%
2 3
 
1.5%
3 2
 
1.0%
4 2
 
1.0%
6 1
 
0.5%
8 2
 
1.0%
10 1
 
0.5%
ValueCountFrequency (%)
18 1
 
0.5%
13 1
 
0.5%
12 1
 
0.5%
11 1
 
0.5%
10 1
 
0.5%
8 2
1.0%
6 1
 
0.5%
4 2
1.0%
3 2
1.0%
2 3
1.5%

Gigartina sp. (língua do diabo)
Real number (ℝ)

Distinct13
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77412935
Minimum0
Maximum50
Zeros180
Zeros (%)89.6%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:20.569194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.3154637
Coefficient of variation (CV)5.5746029
Kurtosis90.068527
Mean0.77412935
Median Absolute Deviation (MAD)0
Skewness8.7832658
Sum155.6
Variance18.623227
MonotonicityNot monotonic
2023-05-15T17:47:20.645352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 180
89.6%
4 4
 
2.0%
1 4
 
2.0%
0.1 2
 
1.0%
5 2
 
1.0%
0.2 2
 
1.0%
7 1
 
0.5%
10 1
 
0.5%
50 1
 
0.5%
3 1
 
0.5%
Other values (3) 3
 
1.5%
ValueCountFrequency (%)
0 180
89.6%
0.1 2
 
1.0%
0.2 2
 
1.0%
1 4
 
2.0%
3 1
 
0.5%
4 4
 
2.0%
5 2
 
1.0%
7 1
 
0.5%
10 1
 
0.5%
12 1
 
0.5%
ValueCountFrequency (%)
50 1
 
0.5%
22 1
 
0.5%
21 1
 
0.5%
12 1
 
0.5%
10 1
 
0.5%
7 1
 
0.5%
5 2
1.0%
4 4
2.0%
3 1
 
0.5%
1 4
2.0%

Plocamium sp.
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1119403
Minimum0
Maximum6
Zeros193
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:20.717245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.63926263
Coefficient of variation (CV)5.7107462
Kurtosis47.939527
Mean0.1119403
Median Absolute Deviation (MAD)0
Skewness6.619172
Sum22.5
Variance0.40865672
MonotonicityNot monotonic
2023-05-15T17:47:20.781327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 193
96.0%
3 3
 
1.5%
4 1
 
0.5%
2 1
 
0.5%
6 1
 
0.5%
0.5 1
 
0.5%
1 1
 
0.5%
ValueCountFrequency (%)
0 193
96.0%
0.5 1
 
0.5%
1 1
 
0.5%
2 1
 
0.5%
3 3
 
1.5%
4 1
 
0.5%
6 1
 
0.5%
ValueCountFrequency (%)
6 1
 
0.5%
4 1
 
0.5%
3 3
 
1.5%
2 1
 
0.5%
1 1
 
0.5%
0.5 1
 
0.5%
0 193
96.0%

Caulacanthus sp.
Real number (ℝ)

Distinct27
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1517413
Minimum0
Maximum60
Zeros131
Zeros (%)65.2%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:20.860992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile25
Maximum60
Range60
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.7192134
Coefficient of variation (CV)2.3409969
Kurtosis12.051373
Mean4.1517413
Median Absolute Deviation (MAD)0
Skewness3.2678701
Sum834.5
Variance94.463109
MonotonicityNot monotonic
2023-05-15T17:47:20.941555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 131
65.2%
1 9
 
4.5%
2 8
 
4.0%
5 7
 
3.5%
6 6
 
3.0%
3 5
 
2.5%
30 4
 
2.0%
4 4
 
2.0%
20 3
 
1.5%
35 2
 
1.0%
Other values (17) 22
 
10.9%
ValueCountFrequency (%)
0 131
65.2%
1 9
 
4.5%
1.5 1
 
0.5%
2 8
 
4.0%
3 5
 
2.5%
4 4
 
2.0%
5 7
 
3.5%
6 6
 
3.0%
7 1
 
0.5%
8 1
 
0.5%
ValueCountFrequency (%)
60 1
 
0.5%
57 1
 
0.5%
48 1
 
0.5%
35 2
1.0%
30 4
2.0%
27 1
 
0.5%
25 2
1.0%
23 1
 
0.5%
22 1
 
0.5%
21 2
1.0%

Condracanthus
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77363184
Minimum0
Maximum48
Zeros176
Zeros (%)87.6%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:21.022928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum48
Range48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.0132843
Coefficient of variation (CV)5.1875894
Kurtosis98.100805
Mean0.77363184
Median Absolute Deviation (MAD)0
Skewness9.0026029
Sum155.5
Variance16.106451
MonotonicityNot monotonic
2023-05-15T17:47:21.099551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 176
87.6%
1 6
 
3.0%
13 3
 
1.5%
2 3
 
1.5%
4 2
 
1.0%
0.2 2
 
1.0%
12 1
 
0.5%
1.5 1
 
0.5%
7 1
 
0.5%
8 1
 
0.5%
Other values (5) 5
 
2.5%
ValueCountFrequency (%)
0 176
87.6%
0.1 1
 
0.5%
0.2 2
 
1.0%
0.5 1
 
0.5%
1 6
 
3.0%
1.5 1
 
0.5%
2 3
 
1.5%
4 2
 
1.0%
5 1
 
0.5%
7 1
 
0.5%
ValueCountFrequency (%)
48 1
 
0.5%
14 1
 
0.5%
13 3
1.5%
12 1
 
0.5%
8 1
 
0.5%
7 1
 
0.5%
5 1
 
0.5%
4 2
1.0%
2 3
1.5%
1.5 1
 
0.5%

Alga vermelha filamentosa
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
199 
0.2
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 199
99.0%
0.2 2
 
1.0%

Length

2023-05-15T17:47:21.177979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:21.258078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 199
99.0%
0.2 2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 400
66.3%
. 201
33.3%
2 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 400
99.5%
2 2
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 400
66.3%
. 201
33.3%
2 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 400
66.3%
. 201
33.3%
2 2
 
0.3%

Alga vermelha vesicular
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:21.323451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:21.401652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Alga vermelha laminada
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.066169154
Minimum0
Maximum5
Zeros194
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:21.458816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45918379
Coefficient of variation (CV)6.9395445
Kurtosis77.544326
Mean0.066169154
Median Absolute Deviation (MAD)0
Skewness8.3761421
Sum13.3
Variance0.21084975
MonotonicityNot monotonic
2023-05-15T17:47:21.529919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 194
96.5%
2 2
 
1.0%
5 1
 
0.5%
0.2 1
 
0.5%
1 1
 
0.5%
3 1
 
0.5%
0.1 1
 
0.5%
ValueCountFrequency (%)
0 194
96.5%
0.1 1
 
0.5%
0.2 1
 
0.5%
1 1
 
0.5%
2 2
 
1.0%
3 1
 
0.5%
5 1
 
0.5%
ValueCountFrequency (%)
5 1
 
0.5%
3 1
 
0.5%
2 2
 
1.0%
1 1
 
0.5%
0.2 1
 
0.5%
0.1 1
 
0.5%
0 194
96.5%

Alga vermelha carnuda
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
0.1
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
0.1 1
 
0.5%

Length

2023-05-15T17:47:21.603461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:21.685109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
0.1 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
1 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
1 1
 
0.2%

Hildenbrandia sp. (incrustante vermelha)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10049751
Minimum0
Maximum4
Zeros189
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:21.745928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.5
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.52445186
Coefficient of variation (CV)5.2185556
Kurtosis39.060862
Mean0.10049751
Median Absolute Deviation (MAD)0
Skewness6.1519751
Sum20.2
Variance0.27504975
MonotonicityNot monotonic
2023-05-15T17:47:21.815526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 189
94.0%
0.5 4
 
2.0%
1 2
 
1.0%
3 2
 
1.0%
4 2
 
1.0%
0.2 1
 
0.5%
2 1
 
0.5%
ValueCountFrequency (%)
0 189
94.0%
0.2 1
 
0.5%
0.5 4
 
2.0%
1 2
 
1.0%
2 1
 
0.5%
3 2
 
1.0%
4 2
 
1.0%
ValueCountFrequency (%)
4 2
 
1.0%
3 2
 
1.0%
2 1
 
0.5%
1 2
 
1.0%
0.5 4
 
2.0%
0.2 1
 
0.5%
0 189
94.0%

Chondria coerulescens (alga verde/azul filamentosa)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
193 
0.1
 
5
0.5
 
2
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 193
96.0%
0.1 5
 
2.5%
0.5 2
 
1.0%
1.0 1
 
0.5%

Length

2023-05-15T17:47:21.894485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:21.981315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 193
96.0%
0.1 5
 
2.5%
0.5 2
 
1.0%
1.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 394
65.3%
. 201
33.3%
1 6
 
1.0%
5 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 394
98.0%
1 6
 
1.5%
5 2
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 394
65.3%
. 201
33.3%
1 6
 
1.0%
5 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 394
65.3%
. 201
33.3%
1 6
 
1.0%
5 2
 
0.3%

Alga vermelha "encaracolada" (que parece ceramium pequeno)
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:22.050780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:22.129468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Alga vermelha semelhante a Ulva rigida
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
199 
0.2
 
1
0.1
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 199
99.0%
0.2 1
 
0.5%
0.1 1
 
0.5%

Length

2023-05-15T17:47:22.191772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:22.274966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 199
99.0%
0.2 1
 
0.5%
0.1 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 400
66.3%
. 201
33.3%
2 1
 
0.2%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 400
99.5%
2 1
 
0.2%
1 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 400
66.3%
. 201
33.3%
2 1
 
0.2%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 400
66.3%
. 201
33.3%
2 1
 
0.2%
1 1
 
0.2%

Porphira
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
199 
0.2
 
1
0.3
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 199
99.0%
0.2 1
 
0.5%
0.3 1
 
0.5%

Length

2023-05-15T17:47:22.341516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:22.425581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 199
99.0%
0.2 1
 
0.5%
0.3 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 400
66.3%
. 201
33.3%
2 1
 
0.2%
3 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 400
99.5%
2 1
 
0.2%
3 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 400
66.3%
. 201
33.3%
2 1
 
0.2%
3 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 400
66.3%
. 201
33.3%
2 1
 
0.2%
3 1
 
0.2%

Osmundea pinnatifida
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
0.2
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
0.2 1
 
0.5%

Length

2023-05-15T17:47:22.491808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:22.578614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
0.2 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
2 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
2 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
2 1
 
0.2%

Alga vermelha ramificada (Polysiphonia?)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
1.0 1
 
0.5%

Length

2023-05-15T17:47:22.641730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:22.722552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
1.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
1 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
1 1
 
0.2%

Ahnfeltiopsis devoniensis
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:22.787453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:22.864406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Stypocaulon scoparium (alga verde tufosa que parece pinheiro)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12338308
Minimum0
Maximum10
Zeros193
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:22.924099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.95163044
Coefficient of variation (CV)7.7128112
Kurtosis82.413494
Mean0.12338308
Median Absolute Deviation (MAD)0
Skewness8.8876351
Sum24.8
Variance0.9056005
MonotonicityNot monotonic
2023-05-15T17:47:22.991913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 193
96.0%
0.2 2
 
1.0%
0.1 1
 
0.5%
10 1
 
0.5%
8 1
 
0.5%
0.3 1
 
0.5%
4 1
 
0.5%
2 1
 
0.5%
ValueCountFrequency (%)
0 193
96.0%
0.1 1
 
0.5%
0.2 2
 
1.0%
0.3 1
 
0.5%
2 1
 
0.5%
4 1
 
0.5%
8 1
 
0.5%
10 1
 
0.5%
ValueCountFrequency (%)
10 1
 
0.5%
8 1
 
0.5%
4 1
 
0.5%
2 1
 
0.5%
0.3 1
 
0.5%
0.2 2
 
1.0%
0.1 1
 
0.5%
0 193
96.0%

Ulva rigida
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7044776
Minimum0
Maximum90
Zeros117
Zeros (%)58.2%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:23.069962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile15
Maximum90
Range90
Interquartile range (IQR)1

Descriptive statistics

Standard deviation9.2143654
Coefficient of variation (CV)3.4070777
Kurtosis46.923228
Mean2.7044776
Median Absolute Deviation (MAD)0
Skewness6.1769867
Sum543.6
Variance84.90453
MonotonicityNot monotonic
2023-05-15T17:47:23.148743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 117
58.2%
0.1 14
 
7.0%
2 13
 
6.5%
0.5 10
 
5.0%
1 10
 
5.0%
3 5
 
2.5%
4 5
 
2.5%
5 4
 
2.0%
20 2
 
1.0%
0.2 2
 
1.0%
Other values (18) 19
 
9.5%
ValueCountFrequency (%)
0 117
58.2%
0.1 14
 
7.0%
0.2 2
 
1.0%
0.3 1
 
0.5%
0.5 10
 
5.0%
1 10
 
5.0%
2 13
 
6.5%
2.5 1
 
0.5%
3 5
 
2.5%
4 5
 
2.5%
ValueCountFrequency (%)
90 1
0.5%
50 1
0.5%
47 1
0.5%
40 1
0.5%
30 1
0.5%
24 1
0.5%
20 2
1.0%
16 1
0.5%
15 2
1.0%
13 1
0.5%

Cladophora sp. (limo)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4363184
Minimum0
Maximum76
Zeros128
Zeros (%)63.7%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:23.232363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile35
Maximum76
Range76
Interquartile range (IQR)2

Descriptive statistics

Standard deviation11.906335
Coefficient of variation (CV)2.6838325
Kurtosis12.828026
Mean4.4363184
Median Absolute Deviation (MAD)0
Skewness3.512951
Sum891.7
Variance141.76082
MonotonicityNot monotonic
2023-05-15T17:47:23.315674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 128
63.7%
1 10
 
5.0%
4 9
 
4.5%
0.5 5
 
2.5%
2 5
 
2.5%
5 3
 
1.5%
8 3
 
1.5%
30 3
 
1.5%
15 3
 
1.5%
22 2
 
1.0%
Other values (21) 30
 
14.9%
ValueCountFrequency (%)
0 128
63.7%
0.1 2
 
1.0%
0.2 2
 
1.0%
0.3 2
 
1.0%
0.5 5
 
2.5%
1 10
 
5.0%
1.5 1
 
0.5%
2 5
 
2.5%
2.5 1
 
0.5%
3 2
 
1.0%
ValueCountFrequency (%)
76 1
0.5%
60 1
0.5%
54 1
0.5%
50 2
1.0%
46 1
0.5%
43 1
0.5%
40 1
0.5%
38 1
0.5%
36 1
0.5%
35 1
0.5%

Limo N.I.
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99353234
Minimum0
Maximum57
Zeros185
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:23.396735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum57
Range57
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.6810481
Coefficient of variation (CV)5.7180304
Kurtosis60.541455
Mean0.99353234
Median Absolute Deviation (MAD)0
Skewness7.3856209
Sum199.7
Variance32.274308
MonotonicityNot monotonic
2023-05-15T17:47:23.458979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 185
92.0%
2 4
 
2.0%
0.1 2
 
1.0%
1 1
 
0.5%
7 1
 
0.5%
6 1
 
0.5%
30 1
 
0.5%
16 1
 
0.5%
57 1
 
0.5%
39 1
 
0.5%
Other values (3) 3
 
1.5%
ValueCountFrequency (%)
0 185
92.0%
0.1 2
 
1.0%
0.5 1
 
0.5%
1 1
 
0.5%
2 4
 
2.0%
6 1
 
0.5%
7 1
 
0.5%
15 1
 
0.5%
16 1
 
0.5%
20 1
 
0.5%
ValueCountFrequency (%)
57 1
 
0.5%
39 1
 
0.5%
30 1
 
0.5%
20 1
 
0.5%
16 1
 
0.5%
15 1
 
0.5%
7 1
 
0.5%
6 1
 
0.5%
2 4
2.0%
1 1
 
0.5%

Ulva intestinalis
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63631841
Minimum0
Maximum41
Zeros188
Zeros (%)93.5%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:23.528351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.5
Maximum41
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.9216992
Coefficient of variation (CV)6.1631082
Kurtosis68.513029
Mean0.63631841
Median Absolute Deviation (MAD)0
Skewness7.8729205
Sum127.9
Variance15.379724
MonotonicityNot monotonic
2023-05-15T17:47:23.592949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 188
93.5%
7 3
 
1.5%
1 2
 
1.0%
0.2 2
 
1.0%
23 1
 
0.5%
41 1
 
0.5%
0.5 1
 
0.5%
2 1
 
0.5%
25 1
 
0.5%
13 1
 
0.5%
ValueCountFrequency (%)
0 188
93.5%
0.2 2
 
1.0%
0.5 1
 
0.5%
1 2
 
1.0%
2 1
 
0.5%
7 3
 
1.5%
13 1
 
0.5%
23 1
 
0.5%
25 1
 
0.5%
41 1
 
0.5%
ValueCountFrequency (%)
41 1
 
0.5%
25 1
 
0.5%
23 1
 
0.5%
13 1
 
0.5%
7 3
 
1.5%
2 1
 
0.5%
1 2
 
1.0%
0.5 1
 
0.5%
0.2 2
 
1.0%
0 188
93.5%

Ulva sp.
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
0.5
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
0.5 1
 
0.5%

Length

2023-05-15T17:47:23.661234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:23.740556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
0.5 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
5 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Codium sp. (alga verde carnuda)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
199 
0.1
 
1
0.2
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 199
99.0%
0.1 1
 
0.5%
0.2 1
 
0.5%

Length

2023-05-15T17:47:23.804952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:23.886786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 199
99.0%
0.1 1
 
0.5%
0.2 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 400
66.3%
. 201
33.3%
1 1
 
0.2%
2 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 400
99.5%
1 1
 
0.2%
2 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 400
66.3%
. 201
33.3%
1 1
 
0.2%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 400
66.3%
. 201
33.3%
1 1
 
0.2%
2 1
 
0.2%

Alga verde carnuda ramificada
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:23.952979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:24.028524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Nemoderma sp.(alga tipo musgo)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
2.0 1
 
0.5%

Length

2023-05-15T17:47:24.089849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:24.167978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
2.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
2 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
2 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
2 1
 
0.2%

alga verde tufosa
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
0.2
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
0.2 1
 
0.5%

Length

2023-05-15T17:47:24.231554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:24.309684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
0.2 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
2 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
2 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
2 1
 
0.2%

alga verde (tipo couve flor)
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:24.373695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:24.450204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Alga verde incrustante
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
5.0 1
 
0.5%

Length

2023-05-15T17:47:24.509892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:24.589892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
5.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
5 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Alga verde n.i.
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:24.653359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:24.730376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Alga verde filamentosa
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
198 
3.0
 
2
9.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 198
98.5%
3.0 2
 
1.0%
9.0 1
 
0.5%

Length

2023-05-15T17:47:24.790812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:25.084363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 198
98.5%
3.0 2
 
1.0%
9.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
3 2
 
0.3%
9 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 399
99.3%
3 2
 
0.5%
9 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
3 2
 
0.3%
9 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
3 2
 
0.3%
9 1
 
0.2%

Alga amarela tipo codium viscoso
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
10.0
 
1

Length

Max length4
Median length3
Mean length3.0049751
Min length3

Characters and Unicode

Total characters604
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
10.0 1
 
0.5%

Length

2023-05-15T17:47:25.145296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:25.220270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
10.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 402
66.6%
. 201
33.3%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 403
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
99.8%
1 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 604
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.6%
. 201
33.3%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 604
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.6%
. 201
33.3%
1 1
 
0.2%

Cladostephus spongiosus (codium que parece que tem areia)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
198 
1.0
 
2
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 198
98.5%
1.0 2
 
1.0%
4.0 1
 
0.5%

Length

2023-05-15T17:47:25.279394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:25.360350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 198
98.5%
1.0 2
 
1.0%
4.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
1 2
 
0.3%
4 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 399
99.3%
1 2
 
0.5%
4 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
1 2
 
0.3%
4 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
1 2
 
0.3%
4 1
 
0.2%

Codium adhaerens (tipo nenúfar)
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:25.426426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:25.504724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Colpomenia sinuosa (alga bolhas)
Real number (ℝ)

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11243781
Minimum0
Maximum3
Zeros163
Zeros (%)81.1%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:25.561533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37642334
Coefficient of variation (CV)3.3478359
Kurtosis34.426274
Mean0.11243781
Median Absolute Deviation (MAD)0
Skewness5.2970077
Sum22.6
Variance0.14169453
MonotonicityNot monotonic
2023-05-15T17:47:25.627406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 163
81.1%
0.1 13
 
6.5%
1 11
 
5.5%
0.5 5
 
2.5%
0.2 4
 
2.0%
0.3 2
 
1.0%
3 2
 
1.0%
0.4 1
 
0.5%
ValueCountFrequency (%)
0 163
81.1%
0.1 13
 
6.5%
0.2 4
 
2.0%
0.3 2
 
1.0%
0.4 1
 
0.5%
0.5 5
 
2.5%
1 11
 
5.5%
3 2
 
1.0%
ValueCountFrequency (%)
3 2
 
1.0%
1 11
 
5.5%
0.5 5
 
2.5%
0.4 1
 
0.5%
0.3 2
 
1.0%
0.2 4
 
2.0%
0.1 13
 
6.5%
0 163
81.1%

Fucus vesiculosus
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
6.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
6.0 1
 
0.5%

Length

2023-05-15T17:47:25.704376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:25.785237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
6.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
6 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
6 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
6 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
6 1
 
0.2%

Dictyota dichotoma (alga azul laminada)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
198 
0.1
 
2
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 198
98.5%
0.1 2
 
1.0%
4.0 1
 
0.5%

Length

2023-05-15T17:47:25.848941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:25.931386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 198
98.5%
0.1 2
 
1.0%
4.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
1 2
 
0.3%
4 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 399
99.3%
1 2
 
0.5%
4 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
1 2
 
0.3%
4 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 399
66.2%
. 201
33.3%
1 2
 
0.3%
4 1
 
0.2%

Dictyota castanha
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
197 
0.1
 
2
0.2
 
1
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 197
98.0%
0.1 2
 
1.0%
0.2 1
 
0.5%
1.0 1
 
0.5%

Length

2023-05-15T17:47:25.998129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:26.081155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 197
98.0%
0.1 2
 
1.0%
0.2 1
 
0.5%
1.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 398
66.0%
. 201
33.3%
1 3
 
0.5%
2 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 398
99.0%
1 3
 
0.7%
2 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 398
66.0%
. 201
33.3%
1 3
 
0.5%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 398
66.0%
. 201
33.3%
1 3
 
0.5%
2 1
 
0.2%

Alga castanha sp.
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
200 
0.5
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200
99.5%
0.5 1
 
0.5%

Length

2023-05-15T17:47:26.151818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:26.231104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200
99.5%
0.5 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
99.8%
5 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
66.5%
. 201
33.3%
5 1
 
0.2%

Alga castanha laminada
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:26.296221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:26.373183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Alga castanha filamentosa
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
197 
2.0
 
2
1.0
 
1
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 197
98.0%
2.0 2
 
1.0%
1.0 1
 
0.5%
3.0 1
 
0.5%

Length

2023-05-15T17:47:26.435484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:26.521959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 197
98.0%
2.0 2
 
1.0%
1.0 1
 
0.5%
3.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 398
66.0%
. 201
33.3%
2 2
 
0.3%
1 1
 
0.2%
3 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 398
99.0%
2 2
 
0.5%
1 1
 
0.2%
3 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 398
66.0%
. 201
33.3%
2 2
 
0.3%
1 1
 
0.2%
3 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 398
66.0%
. 201
33.3%
2 2
 
0.3%
1 1
 
0.2%
3 1
 
0.2%

Alga castanha incrustante
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:26.594668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:26.673381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Alga castanha carnuda
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:26.736936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:26.815741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Alga castanha tufosa
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:26.876051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:26.954658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Alga verde/azul - lavanda
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:27.015108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:27.093634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Sphacelaria rigidula (pompons castanhos)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2686567
Minimum0
Maximum50
Zeros182
Zeros (%)90.5%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:27.156458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.8185018
Coefficient of variation (CV)4.5863484
Kurtosis38.618271
Mean1.2686567
Median Absolute Deviation (MAD)0
Skewness5.917844
Sum255
Variance33.854963
MonotonicityNot monotonic
2023-05-15T17:47:27.235424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 182
90.5%
10 2
 
1.0%
4 2
 
1.0%
0.5 2
 
1.0%
30 1
 
0.5%
25 1
 
0.5%
23 1
 
0.5%
6 1
 
0.5%
5 1
 
0.5%
1 1
 
0.5%
Other values (7) 7
 
3.5%
ValueCountFrequency (%)
0 182
90.5%
0.5 2
 
1.0%
1 1
 
0.5%
2 1
 
0.5%
3 1
 
0.5%
4 2
 
1.0%
5 1
 
0.5%
6 1
 
0.5%
8 1
 
0.5%
10 2
 
1.0%
ValueCountFrequency (%)
50 1
0.5%
40 1
0.5%
30 1
0.5%
25 1
0.5%
23 1
0.5%
20 1
0.5%
13 1
0.5%
10 2
1.0%
8 1
0.5%
6 1
0.5%

Cystoseira sp.
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size11.2 KiB

Laminaria sp.
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201
100.0%

Length

2023-05-15T17:47:27.312296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:27.389645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201
100.0%

Most occurring characters

ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
66.7%
Other Punctuation 201
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 402
66.7%
. 201
33.3%

TOTAL2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct155
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.940149
Minimum0
Maximum100
Zeros32
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-05-15T17:47:27.467501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.1
median56.2
Q384.2
95-th percentile98.7
Maximum100
Range100
Interquartile range (IQR)71.1

Descriptive statistics

Standard deviation35.722999
Coefficient of variation (CV)0.70127393
Kurtosis-1.4156953
Mean50.940149
Median Absolute Deviation (MAD)33
Skewness-0.20106437
Sum10238.97
Variance1276.1326
MonotonicityNot monotonic
2023-05-15T17:47:27.566942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
15.9%
99.6 2
 
1.0%
56.2 2
 
1.0%
45.1 2
 
1.0%
76.1 2
 
1.0%
98.2 2
 
1.0%
49.1 2
 
1.0%
23 2
 
1.0%
83 2
 
1.0%
1 2
 
1.0%
Other values (145) 151
75.1%
ValueCountFrequency (%)
0 32
15.9%
0.01 1
 
0.5%
0.1 1
 
0.5%
0.2 1
 
0.5%
0.5 2
 
1.0%
0.7 1
 
0.5%
1 2
 
1.0%
2 1
 
0.5%
3 1
 
0.5%
6.8 1
 
0.5%
ValueCountFrequency (%)
100 2
1.0%
99.8 1
0.5%
99.7 1
0.5%
99.6 2
1.0%
99.4 1
0.5%
99.3 1
0.5%
99.1 1
0.5%
99 1
0.5%
98.7 1
0.5%
98.5 1
0.5%

observações
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing161
Missing (%)80.1%
Memory size11.2 KiB

Coluna1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)2.9%
Missing167
Missing (%)83.1%
Memory size11.2 KiB
0.0
34 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters102
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 34
 
16.9%
(Missing) 167
83.1%

Length

2023-05-15T17:47:27.653896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:27.730192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 34
100.0%

Most occurring characters

ValueCountFrequency (%)
0 68
66.7%
. 34
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68
66.7%
Other Punctuation 34
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68
100.0%
Other Punctuation
ValueCountFrequency (%)
. 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68
66.7%
. 34
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68
66.7%
. 34
33.3%

Coluna2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)2.9%
Missing167
Missing (%)83.1%
Memory size11.2 KiB
0.0
34 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters102
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 34
 
16.9%
(Missing) 167
83.1%

Length

2023-05-15T17:47:27.792163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T17:47:27.868314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 34
100.0%

Most occurring characters

ValueCountFrequency (%)
0 68
66.7%
. 34
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68
66.7%
Other Punctuation 34
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68
100.0%
Other Punctuation
ValueCountFrequency (%)
. 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68
66.7%
. 34
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68
66.7%
. 34
33.3%

Interactions

2023-05-15T17:47:08.714893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:47.397136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:49.685469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:52.259487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:54.423812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:56.975544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:59.510813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:01.789100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:04.287794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:06.583336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:09.237692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:11.513981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:14.070583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:16.477415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:19.135970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:21.516966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:24.161544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:26.703282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:29.520913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:32.032492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:34.880642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:37.613840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:40.080454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:42.728448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:45.327431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:47.946417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:50.604696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:53.326005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:55.765469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-15T17:45:54.299185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:56.844289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:59.161116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:01.635504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:04.156162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:06.448055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:09.095122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:11.383660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:13.934164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:16.342505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:18.996697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:21.378356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:24.018990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:26.551272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:29.376651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:31.891484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:34.727432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:37.478496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:39.934954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:42.586905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:45.184043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:47.801641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:50.442156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:53.173564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:55.626730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:58.435707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:47:00.833699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:47:03.383599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:47:05.865728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:47:08.576134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:47:11.062380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:49.616877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:52.192921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:54.361970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:56.910943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:59.226421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:01.711098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:04.222113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:06.515962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:09.166052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:11.449016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:14.003929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:16.410476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:19.067064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:21.449476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:24.091754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:26.623914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:29.449417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:31.962840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:34.804307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:37.544425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:40.008165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:42.657412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:45.256066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:47.871908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:50.520429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:53.248344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:55.698025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:58.509688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:47:00.905126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:47:03.450866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:47:05.941133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:47:08.645524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-15T17:47:28.005548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
TideSiphonaria algesiraeGibbula sp.Monodonta lineataLittorina neritoidesMytillus sp.Actinia equinaAnemonia sulcataParacentrotus lividusHymeniacidon sanguineaVerrucaria mauraLichina pygmaea (líquene folhoso)Coralina elongataLithophillum incrustansGelidium sp. (tipo raquete)Asparagopsis armata (tufosa)Asparagopsis armata (adulta)Ceramium (forma taças)Mastocarpus sp. (alga preta só mediolitoral)Gigartina sp. (língua do diabo)Plocamium sp.Caulacanthus sp.CondracanthusAlga vermelha laminadaHildenbrandia sp. (incrustante vermelha)Stypocaulon scoparium (alga verde tufosa que parece pinheiro)Ulva rigidaCladophora sp. (limo)Limo N.I.Ulva intestinalisColpomenia sinuosa (alga bolhas)Sphacelaria rigidula (pompons castanhos)TOTAL2Weather ConditionSamplerZoneSupratidal/Middle IntertidalSubstrateNassariidae (búzio)Ophiothrix sp.Sabellaria alveolata (tubos)Anémona branca com estrias (fam. Cerianthidae)Polysyncraton sp. (ascídias vermelhas)Didemnum sp. (ascídeas brancas)Burrié negro N.I.Búzio/burrié n.i.Watersipora subtorquata (Ascídea)Litophylum lichenoidesLitophylum tortuosumMesophylum lichenoidesPorphyra sp.Alga vermelha filamentosaAlga vermelha carnudaChondria coerulescens (alga verde/azul filamentosa)Alga vermelha semelhante a Ulva rigidaPorphiraOsmundea pinnatifidaAlga vermelha ramificada (Polysiphonia?)Ulva sp.Codium sp. (alga verde carnuda)Nemoderma sp.(alga tipo musgo)alga verde tufosaAlga verde incrustanteAlga verde filamentosaAlga amarela tipo codium viscosoCladostephus spongiosus (codium que parece que tem areia)Fucus vesiculosusDictyota dichotoma (alga azul laminada)Dictyota castanhaAlga castanha sp.Alga castanha filamentosa
Tide1.0000.0380.006-0.004-0.125-0.089-0.1230.1900.0310.047-0.0530.007-0.091-0.154-0.017-0.1310.030-0.0650.0110.035-0.106-0.111-0.022-0.032-0.096-0.051-0.217-0.010-0.004-0.006-0.001-0.037-0.1440.1790.1440.1260.0000.1460.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.2330.0000.0000.0000.3090.0000.0000.0000.3070.0000.0000.0000.0000.0000.3070.0000.0000.0000.0000.5350.073
Siphonaria algesirae0.0381.0000.2640.3350.1100.3630.1750.002-0.065-0.0640.1710.206-0.1750.0930.182-0.075-0.098-0.1640.1650.140-0.1190.008-0.133-0.0530.027-0.119-0.131-0.0540.156-0.062-0.1970.0980.0500.0000.0000.2210.0810.0000.0000.0000.0000.0000.0000.0000.0000.4020.0000.0000.0000.0000.0000.0000.0000.1770.0000.0000.0000.0000.0000.0000.4020.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Gibbula sp.0.0060.2641.0000.282-0.0240.3460.1440.0530.1820.083-0.0070.0830.1420.3690.273-0.0620.1300.0540.3040.047-0.0130.2200.1020.0660.1630.0750.0880.1760.064-0.1150.078-0.0750.2380.0000.0000.0330.0890.4270.6130.3720.0000.5600.3720.9900.0000.0000.0000.0000.4450.0000.0000.0000.0000.1260.0000.0000.0000.0000.0000.0000.0000.3720.0000.0000.0000.0000.0000.0000.0000.0000.000
Monodonta lineata-0.0040.3350.2821.0000.1370.2130.230-0.058-0.103-0.0900.0280.296-0.1320.2020.0900.014-0.031-0.0910.077-0.0470.021-0.060-0.114-0.0580.173-0.062-0.086-0.0920.242-0.080-0.146-0.0410.0570.0000.0000.0000.1300.1050.2320.0000.0000.0000.0000.5490.0000.0000.0000.0000.6480.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.9870.0000.0000.0000.0000.0000.0000.0000.0000.000
Littorina neritoides-0.1250.110-0.0240.1371.000-0.0230.155-0.079-0.142-0.1400.1840.210-0.324-0.159-0.152-0.026-0.127-0.184-0.059-0.104-0.085-0.110-0.157-0.0790.022-0.085-0.097-0.1310.0270.226-0.200-0.039-0.0640.1490.1620.0960.2470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Mytillus sp.-0.0890.3630.3460.213-0.0231.0000.3890.1160.1290.2080.0910.1100.1470.2890.441-0.077-0.0670.0170.1110.0300.1540.244-0.072-0.0410.076-0.0120.2000.1120.054-0.1120.090-0.0250.3770.0000.0000.0620.0800.0000.0000.0000.0000.4420.0000.0000.4850.0000.4600.0000.0000.2510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3450.3220.1640.0000.164
Actinia equina-0.1230.1750.1440.2300.1550.3891.0000.0800.0780.0840.1230.1370.0610.3130.1850.0490.0130.011-0.138-0.063-0.0160.098-0.0820.1380.0140.1190.1490.0360.027-0.110-0.075-0.0500.2190.0000.0000.0000.1310.0000.0000.0000.0000.5780.0000.0000.0000.0000.0000.2590.5460.0000.0000.0000.0000.0000.0000.0000.0000.2590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.561
Anemonia sulcata0.1900.0020.053-0.058-0.0790.1160.0801.0000.4930.068-0.039-0.0300.1480.2730.252-0.0410.331-0.021-0.003-0.065-0.0390.031-0.071-0.0360.062-0.0390.0990.044-0.056-0.0500.1890.0230.1510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.4860.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Paracentrotus lividus0.031-0.0650.182-0.103-0.1420.1290.0780.4931.0000.068-0.069-0.0540.2640.4970.3210.1580.4250.135-0.010-0.1160.0070.1690.0160.1210.1130.2410.1480.289-0.1000.1010.3070.0030.3230.1090.0710.0000.1340.3320.0000.0000.0000.0000.4150.0000.0000.0000.9680.0000.0000.6930.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4150.3140.0000.0000.241
Hymeniacidon sanguinea0.047-0.0640.083-0.090-0.1400.2080.0840.0680.0681.000-0.055-0.0270.5460.2220.2130.0000.1330.0050.0070.0940.1160.2400.1510.0060.0210.0810.3930.073-0.165-0.0520.2000.0260.4120.0000.0000.0000.0810.3950.0000.0000.2860.1050.0000.0000.0000.0000.0000.2860.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1660.0000.240
Verrucaria maura-0.0530.171-0.0070.0280.1840.0910.123-0.039-0.069-0.0551.000-0.033-0.137-0.056-0.039-0.044-0.062-0.090-0.0850.015-0.0410.009-0.076-0.039-0.051-0.0410.0210.0020.2220.159-0.0980.028-0.0020.0000.0000.0000.0970.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Lichina pygmaea (líquene folhoso)0.0070.2060.0830.2960.2100.1100.137-0.030-0.054-0.027-0.0331.000-0.1310.068-0.016-0.035-0.048-0.0700.027-0.054-0.0330.017-0.060-0.0300.094-0.033-0.044-0.053-0.047-0.042-0.077-0.0520.1140.0000.0000.2730.0650.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Coralina elongata-0.091-0.1750.142-0.132-0.3240.1470.0610.1480.2640.546-0.137-0.1311.0000.5020.2560.1270.2300.2850.0710.1060.2170.3760.3010.1960.1110.2320.5350.232-0.236-0.0800.4670.0890.6560.0000.0000.0450.5780.1850.0000.0160.1310.2570.1310.0000.1800.0000.5300.0000.0000.1090.0000.0000.1310.3090.3260.0000.1310.0000.0160.4550.0000.0000.0000.0000.0160.1020.0160.0000.2690.0000.241
Lithophillum incrustans-0.1540.0930.3690.202-0.1590.2890.3130.2730.4970.222-0.0560.0680.5021.0000.3490.1840.3150.2650.0770.0280.2190.2540.0010.1950.2530.2410.3250.199-0.102-0.0240.3220.1420.4460.0000.1280.0540.2350.1170.0000.0000.0000.0000.0890.0000.0000.0000.5100.3150.5870.2660.0000.0000.0000.1400.0000.0000.3510.0890.0000.0000.0000.3150.0000.0000.0000.0000.0890.2730.1160.0000.087
Gelidium sp. (tipo raquete)-0.0170.1820.2730.090-0.1520.4410.1850.2520.3210.213-0.039-0.0160.2560.3491.000-0.0130.0300.0100.0990.0280.0110.1500.0850.0340.090-0.0570.1590.0790.017-0.0400.113-0.0160.3010.0000.0690.0500.1590.4070.4800.0000.0000.0000.0000.9900.0000.0000.4600.0000.0000.2710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4150.1490.0000.0000.000
Asparagopsis armata (tufosa)-0.131-0.075-0.0620.014-0.026-0.0770.049-0.0410.1580.000-0.044-0.0350.1270.184-0.0131.0000.3720.2500.0460.0000.077-0.017-0.0040.2190.0430.0830.159-0.019-0.0640.0390.0630.0960.1030.0000.0000.0000.0510.0000.0000.0000.0000.0000.5670.0000.0000.0000.0000.0000.0000.4860.0000.0000.0000.1220.0000.0000.0000.5670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Asparagopsis armata (adulta)0.030-0.0980.130-0.031-0.127-0.0670.0130.3310.4250.133-0.062-0.0480.2300.3150.0300.3721.0000.1910.017-0.0540.0310.013-0.0030.0370.1390.1170.1960.151-0.089-0.0030.2560.0190.1410.0000.1040.0200.0000.6680.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.3300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Ceramium (forma taças)-0.065-0.1640.054-0.091-0.1840.0170.011-0.0210.1350.005-0.090-0.0700.2850.2650.0100.2500.1911.000-0.017-0.0090.1900.1380.1290.2310.1510.0530.3620.293-0.130-0.0660.4290.0460.3490.0000.1440.0000.1660.0720.0000.0000.0000.0000.3900.0000.1270.0000.0000.0000.0000.0000.0000.0000.6740.3670.0000.0000.3900.6740.0000.2320.3900.0000.0000.0000.0000.4540.0000.3180.0000.0000.000
Mastocarpus sp. (alga preta só mediolitoral)0.0110.1650.3040.077-0.0590.111-0.138-0.003-0.0100.007-0.0850.0270.0710.0770.0990.0460.017-0.0171.0000.097-0.0850.1710.181-0.0790.246-0.0850.0670.1580.029-0.1100.0810.0870.1000.0000.1300.0000.0000.0000.0000.0000.0000.0000.0000.0000.1440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Gigartina sp. (língua do diabo)0.0350.1400.047-0.047-0.1040.030-0.063-0.065-0.1160.0940.015-0.0540.1060.0280.0280.000-0.054-0.0090.0971.0000.008-0.0200.1160.019-0.0860.006-0.0270.0600.027-0.090-0.1300.0570.0530.1310.1640.0000.0000.0000.0000.0000.0000.0000.0000.0000.2710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Plocamium sp.-0.106-0.119-0.0130.021-0.0850.154-0.016-0.0390.0070.116-0.041-0.0330.2170.2190.0110.0770.0310.190-0.0850.0081.0000.143-0.003-0.039-0.0510.2130.1830.054-0.0600.1490.1450.2110.1440.0000.0000.0000.0000.0000.0360.0000.0000.2940.0000.0000.0000.0001.0000.0000.0000.0000.5600.0000.9900.3790.0000.0000.0000.0000.9900.0000.0000.0000.0000.0000.9900.0000.0000.4760.6930.0000.000
Caulacanthus sp.-0.1110.0080.220-0.060-0.1100.2440.0980.0310.1690.2400.0090.0170.3760.2540.150-0.0170.0130.1380.171-0.0200.1431.0000.2150.0070.0490.1590.3090.247-0.064-0.1400.215-0.0510.4180.2300.1830.0000.2420.0000.0000.0000.3230.0000.3230.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3230.0000.0000.0000.0000.0000.0000.0000.0000.0000.3230.1880.3090.0000.000
Condracanthus-0.022-0.1330.102-0.114-0.157-0.072-0.082-0.0710.0160.151-0.076-0.0600.3010.0010.085-0.004-0.0030.1290.1810.116-0.0030.2151.0000.012-0.0290.0770.2400.169-0.110-0.0420.2430.0420.2560.0000.0000.0000.1190.0000.1240.0000.4270.0000.5630.0000.0000.0000.0000.0000.0000.1290.0000.0000.0000.2670.0000.0000.4270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1980.0000.000
Alga vermelha laminada-0.032-0.0530.066-0.058-0.079-0.0410.138-0.0360.1210.006-0.039-0.0300.1960.1950.0340.2190.0370.231-0.0790.019-0.0390.0070.0121.0000.0640.2360.124-0.050-0.0560.0620.1920.1220.1640.0000.2520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9520.0000.0000.3850.0000.0000.0000.0000.0000.0000.0000.9920.0000.0000.0000.0000.0000.0000.0000.0000.0000.4810.0000.0000.000
Hildenbrandia sp. (incrustante vermelha)-0.0960.0270.1630.1730.0220.0760.0140.0620.1130.021-0.0510.0940.1110.2530.0900.0430.1390.1510.246-0.086-0.0510.049-0.0290.0641.0000.0530.068-0.0260.003-0.0660.092-0.0180.1700.2160.0760.0000.0540.1830.0000.0000.0000.3780.0000.0000.0000.0000.0000.0000.3780.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.3150.0000.6890.0000.6930.0000.0000.0000.0000.0000.0000.000
Stypocaulon scoparium (alga verde tufosa que parece pinheiro)-0.051-0.1190.075-0.062-0.085-0.0120.119-0.0390.2410.081-0.041-0.0330.2320.241-0.0570.0830.1170.053-0.0850.0060.2130.1590.0770.2360.0531.0000.1650.142-0.0600.0440.2440.0190.1220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3850.0000.0000.0000.0000.0000.0000.9920.9920.0000.0000.0000.0000.0000.4810.0000.4810.0000.0000.0000.0000.000
Ulva rigida-0.217-0.1310.088-0.086-0.0970.2000.1490.0990.1480.3930.021-0.0440.5350.3250.1590.1590.1960.3620.067-0.0270.1830.3090.2400.1240.0680.1651.0000.229-0.180-0.0890.3530.0520.5350.0000.0000.1040.1550.0000.0000.0000.5490.0000.0000.0000.0000.0000.0000.0000.0000.3720.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2260.0000.0000.0000.0000.1300.0000.372
Cladophora sp. (limo)-0.010-0.0540.176-0.092-0.1310.1120.0360.0440.2890.0730.002-0.0530.2320.1990.079-0.0190.1510.2930.1580.0600.0540.2470.169-0.050-0.0260.1420.2291.000-0.166-0.0740.2470.2070.3630.1420.1680.0000.2120.0000.1510.6780.0000.0000.0000.0000.3320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.2160.0000.0000.2030.0000.0000.0000.0000.0000.0000.000
Limo N.I.-0.0040.1560.0640.2420.0270.0540.027-0.056-0.100-0.1650.222-0.047-0.236-0.1020.017-0.064-0.089-0.1300.0290.027-0.060-0.064-0.110-0.0560.003-0.060-0.180-0.1661.000-0.001-0.1410.0360.0110.0000.1910.0510.1210.1720.3110.0000.0000.0000.0000.6850.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Ulva intestinalis-0.006-0.062-0.115-0.0800.226-0.112-0.110-0.0500.101-0.0520.159-0.042-0.080-0.024-0.0400.039-0.003-0.066-0.110-0.0900.149-0.140-0.0420.062-0.0660.044-0.089-0.074-0.0011.0000.0190.1330.0320.0000.0000.0000.1160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Colpomenia sinuosa (alga bolhas)-0.001-0.1970.078-0.146-0.2000.090-0.0750.1890.3070.200-0.098-0.0770.4670.3220.1130.0630.2560.4290.081-0.1300.1450.2150.2430.1920.0920.2440.3530.247-0.1410.0191.0000.2050.4010.0240.0000.1200.2240.0000.0000.0000.0000.0000.0000.0000.3860.0000.0000.0000.0000.1060.0000.0000.0000.2090.0000.0000.2680.0000.0000.0000.0000.0000.0000.2140.0000.3420.2680.1550.0000.0000.120
Sphacelaria rigidula (pompons castanhos)-0.0370.098-0.075-0.041-0.039-0.025-0.0500.0230.0030.0260.028-0.0520.0890.142-0.0160.0960.0190.0460.0870.0570.211-0.0510.0420.122-0.0180.0190.0520.2070.0360.1330.2051.0000.2080.0000.1240.0910.0000.0000.2210.0000.6820.0000.0000.0000.3030.0000.0000.0000.0000.0000.2190.0000.0000.4190.0000.0000.0000.0000.4610.0000.0000.0000.4610.1570.4610.6860.0000.4650.0000.0000.000
TOTAL2-0.1440.0500.2380.057-0.0640.3770.2190.1510.3230.412-0.0020.1140.6560.4460.3010.1030.1410.3490.1000.0530.1440.4180.2560.1640.1700.1220.5350.3630.0110.0320.4010.2081.0000.0000.2840.1310.4230.0000.4840.4810.4810.2520.4810.4810.3260.4810.4680.4810.4830.3850.0000.4810.4810.2520.4820.4820.4810.4810.4810.4820.4810.4810.4810.0000.4810.0000.4810.4820.2520.4810.252
Weather Condition0.1790.0000.0000.0000.1490.0000.0000.0000.1090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1310.0000.2300.0000.0000.2160.0000.0000.1420.0000.0000.0240.0000.0001.0000.3720.0520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0910.0000.000
Sampler0.1440.0000.0000.0000.1620.0000.0000.0000.0710.0000.0000.0000.0000.1280.0690.0000.1040.1440.1300.1640.0000.1830.0000.2520.0760.0000.0000.1680.1910.0000.0000.1240.2840.3721.0000.0000.0880.0000.0000.0000.3700.0000.0000.0000.0000.0000.0000.4330.0750.0000.0000.0000.0450.0000.0000.3290.1110.1110.0000.0000.0000.0450.0000.0000.0000.3650.0000.6790.0000.0000.000
Zone0.1260.2210.0330.0000.0960.0620.0000.0000.0000.0000.0000.2730.0450.0540.0500.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.1040.0000.0510.0000.1200.0910.1310.0520.0001.0000.0550.1090.0500.0000.0000.0000.0000.0000.0000.0000.1440.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.0000.0890.0000.0000.0000.0000.000
Supratidal/Middle Intertidal0.0000.0810.0890.1300.2470.0800.1310.0000.1340.0810.0970.0650.5780.2350.1590.0510.0000.1660.0000.0000.0000.2420.1190.0000.0540.0000.1550.2120.1210.1160.2240.0000.4230.0000.0880.0551.0000.3590.0490.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0290.0650.0000.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.000
Substrate0.1460.0000.4270.1050.0000.0000.0000.0000.3320.3950.0000.0000.1850.1170.4070.0000.6680.0720.0000.0000.0000.0000.0000.0000.1830.0000.0000.0000.1720.0000.0000.0000.0000.0000.0000.1090.3591.0000.5740.2050.0000.1260.0000.9690.0000.0000.0000.0000.0000.2330.0000.3870.0000.0000.0000.0000.0000.0000.0000.4390.0000.0000.2050.0000.0000.0000.0000.0000.1430.0000.225
Nassariidae (búzio)0.0000.0000.6130.2320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4800.0000.0000.0000.0000.0000.0360.0000.1240.0000.0000.0000.0000.1510.3110.0000.0000.2210.4840.0000.0000.0500.0490.5741.0000.6930.0000.0000.0000.9920.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1850.0000.000
Ophiothrix sp.0.0000.0000.3720.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6780.0000.0000.0000.0000.4810.0000.0000.0000.0000.2050.6931.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Sabellaria alveolata (tubos)0.0000.0000.0000.0000.0000.0000.0000.0000.0000.2860.0000.0000.1310.0000.0000.0000.0000.0000.0000.0000.0000.3230.4270.0000.0000.0000.5490.0000.0000.0000.0000.6820.4810.0000.3700.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Anémona branca com estrias (fam. Cerianthidae)0.0000.0000.5600.0000.0000.4420.5780.0000.0000.1050.0000.0000.2570.0000.0000.0000.0000.0000.0000.0000.2940.0000.0000.0000.3780.0000.0000.0000.0000.0000.0000.0000.2520.0000.0000.0000.0000.1260.0000.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3910.0000.000
Polysyncraton sp. (ascídias vermelhas)0.0000.0000.3720.0000.0000.0000.0000.0000.4150.0000.0000.0000.1310.0890.0000.5670.0000.3900.0000.0000.0000.3230.5630.0000.0000.0000.0000.0000.0000.0000.0000.0000.4810.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.4270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Didemnum sp. (ascídeas brancas)0.0000.0000.9900.5490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6850.0000.0000.0000.4810.0000.0000.0000.0000.9690.9920.0000.0000.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Burrié negro N.I.0.0350.0000.0000.0000.0000.4850.0000.0000.0000.0000.0000.0000.1800.0000.0000.0000.0000.1270.1440.2710.0000.0340.0000.0000.0000.0000.0000.3320.0000.0000.3860.3030.3260.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.0000.3150.0000.2510.0000.0000.0000.0000.0000.0000.4770.0000.0000.0000.0000.0000.0000.0000.0000.0000.250
Búzio/burrié n.i.0.0000.4020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Watersipora subtorquata (Ascídea)0.0000.0000.0000.0000.0000.4600.0001.0000.9680.0001.0000.0000.5300.5100.4600.0001.0000.0000.0000.0001.0000.0000.0000.9520.0000.0000.0000.0001.0000.0000.0000.0000.4680.0000.0000.1440.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.4601.0001.0001.0000.0001.0000.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.4601.0001.0001.000
Litophylum lichenoides0.0000.0000.0000.0000.0000.0000.2590.0000.0000.2860.0000.0000.0000.3150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4810.0000.4330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Litophylum tortuosum0.0000.0000.4450.6480.0000.0000.5460.0000.0000.0000.0000.5690.0000.5870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3780.0000.0000.0000.0000.0000.0000.0000.4830.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9950.0000.0000.0000.0000.0000.0000.0000.0000.000
Mesophylum lichenoides0.0000.0000.0000.0000.0000.2510.0000.4860.6930.0000.0000.0000.1090.2660.2710.4860.3300.0000.0000.0000.0000.0000.1290.3850.0000.3850.3720.0000.0000.0000.1060.0000.3850.0000.0000.0000.0290.2330.0000.0000.0000.0000.0000.0000.0000.0000.4600.0000.0001.0000.0000.0000.0000.0000.0000.0000.6960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3300.0000.0000.000
Porphyra sp.0.2330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5600.0000.0000.0000.0000.0000.0000.0000.0000.5600.0000.2190.0000.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.3910.0000.0000.0000.0000.9950.0000.0000.0000.0000.0000.9950.0000.0000.0000.0000.0000.000
Alga vermelha filamentosa0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4810.0000.0000.0000.0000.3870.0000.0000.0000.0000.0000.0000.3150.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Alga vermelha carnuda0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1310.0000.0000.0000.0000.6740.0000.0000.9900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4810.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Chondria coerulescens (alga verde/azul filamentosa)0.0000.1770.1260.0410.0000.0000.0000.0000.0290.0000.0000.0000.3090.1400.0000.1220.0000.3670.0000.0000.3790.0000.2670.0000.0820.0000.0000.0000.0000.0000.2090.4190.2520.0000.0000.0270.0000.0000.0000.0000.0000.0000.4270.0000.2510.0000.0000.0000.0000.0000.3910.0000.0001.0000.0000.0000.0000.0000.6960.0000.0000.0000.0000.0000.6960.0000.0000.0000.0000.0000.226
Alga vermelha semelhante a Ulva rigida0.3090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.4820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Porphira0.0000.0000.0000.0000.3890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4820.0000.3290.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Osmundea pinnatifida0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1310.3510.0000.0000.0000.3900.0000.0000.0000.3230.4270.0000.0000.9920.0000.0000.0000.0000.2680.0000.4810.0000.1110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.6960.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Alga vermelha ramificada (Polysiphonia?)0.0000.0000.0000.0000.0000.0000.2590.0000.0000.0000.0000.0000.0000.0890.0000.5670.0000.6740.0000.0000.0000.0000.0000.9920.0000.9920.0000.0000.0000.0000.0000.0000.4810.0000.1110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Ulva sp.0.3070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.9900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4610.4810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.9950.0000.0000.6960.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.4940.0000.0000.0000.0000.0000.000
Codium sp. (alga verde carnuda)0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4550.0000.0000.0000.0000.2320.6820.0000.0000.0000.0000.0000.3150.0000.0000.0000.0000.0000.0000.0000.4820.0000.0000.0000.0000.4390.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Nemoderma sp.(alga tipo musgo)0.0000.4020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3900.0000.0000.0000.0000.0000.0000.0000.0000.0000.2160.0000.0000.0000.0000.4810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4770.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
alga verde tufosa0.0000.0000.3720.9870.0000.0000.0000.0000.0000.0000.0000.0000.0000.3150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6890.0000.0000.0000.0000.0000.0000.0000.4810.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.9950.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Alga verde incrustante0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4610.4810.0000.0000.0000.0000.2050.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
Alga verde filamentosa0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6930.4810.2260.2030.0000.0000.2140.1570.0000.0000.0000.0890.0650.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.3370.0000.0000.0000.0000.000
Alga amarela tipo codium viscoso0.3070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.9900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4610.4810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.9950.0000.0000.6960.0000.0000.0000.0000.4940.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
Cladostephus spongiosus (codium que parece que tem areia)0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1020.0000.0000.0000.0000.4540.0000.0000.0000.0000.0000.0000.0000.4810.0000.0000.0000.0000.3420.6860.0000.0000.3650.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3370.0001.0000.0000.0000.0000.0000.000
Fucus vesiculosus0.0000.0000.0000.0000.0000.3450.0000.0000.4150.0000.0000.0000.0160.0890.4150.0000.0000.0000.0000.0000.0000.3230.0000.0000.0000.0000.0000.0000.0000.0000.2680.0000.4810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
Dictyota dichotoma (alga azul laminada)0.0000.0000.0000.0000.0000.3220.0000.0000.3140.0000.0000.0000.0000.2730.1490.0000.0000.3180.0000.0000.4760.1880.0000.4810.0000.0000.0000.0000.0000.0000.1550.4650.4820.0000.6790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4600.0000.0000.3300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
Dictyota castanha0.0000.0000.0000.0000.0000.1640.0000.0000.0000.1660.0000.0000.2690.1160.0000.0000.0000.0000.0000.0000.6930.3090.1980.0000.0000.0000.1300.0000.0000.0000.0000.0000.2520.0910.0000.0000.0000.1430.1850.0000.0000.3910.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
Alga castanha sp.0.5350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
Alga castanha filamentosa0.0730.0000.0000.0000.0000.1640.5610.0000.2410.2400.0000.0000.2410.0870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3720.0000.0000.0000.1200.0000.2520.0000.0000.0000.0000.2250.0000.0000.0000.0000.0000.0000.2500.0001.0000.0000.0000.0000.0000.0000.0000.2260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-05-15T17:47:11.533373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-15T17:47:11.968915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-15T17:47:12.359715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateHourTideWeather ConditionWater temperature (ºC)SamplerZoneSupratidal/Middle IntertidalSubstrateChthamalus sp.Balanus perforatusPatella sp.Siphonaria algesiraeGibbula sp.Monodonta lineataLittorina neritoidesMytillus sp.Nassariidae (búzio)Chiton sp.Pollicipes pollicipesActinia equinaAnemonia sulcataOphiothrix sp.Paracentrotus lividusHymeniacidon sanguineaAglaophenia pluma (hidrozoário branco ramificado)Alga branca ramificadaSabellaria alveolata (tubos)CerianthidaeAnémona N.I.Actinia fragaceaGymnangium montaguiAnémona branca com estrias (fam. Cerianthidae)Actinothoe sphyrodeta (anémona branca e laranja)Oncidiella celtica (lesma)Calliostoma sp. (burrié bicudo)Polysyncraton sp. (ascídias vermelhas)Didemnum sp. (ascídeas brancas)Burrié negro N.I.Búzio/burrié n.i.Watersipora subtorquata (Ascídea)Verrucaria mauraLichina pygmaea (líquene folhoso)Coralina elongataLithophillum incrustansLitophylum lichenoidesLitophylum tortuosumMesophylum lichenoidesGelidium sp. (tipo raquete)Asparagopsis armata (tufosa)Asparagopsis armata (adulta)Porphyra sp.Ceramium (forma taças)Mastocarpus sp. (alga preta só mediolitoral)Gigartina sp. (língua do diabo)Plocamium sp.Caulacanthus sp.CondracanthusAlga vermelha filamentosaAlga vermelha vesicularAlga vermelha laminadaAlga vermelha carnudaHildenbrandia sp. (incrustante vermelha)Chondria coerulescens (alga verde/azul filamentosa)Alga vermelha "encaracolada" (que parece ceramium pequeno)Alga vermelha semelhante a Ulva rigidaPorphiraOsmundea pinnatifidaAlga vermelha ramificada (Polysiphonia?)Ahnfeltiopsis devoniensisStypocaulon scoparium (alga verde tufosa que parece pinheiro)Ulva rigidaCladophora sp. (limo)Limo N.I.Ulva intestinalisUlva sp.Codium sp. (alga verde carnuda)Alga verde carnuda ramificadaNemoderma sp.(alga tipo musgo)alga verde tufosaalga verde (tipo couve flor)Alga verde incrustanteAlga verde n.i.Alga verde filamentosaAlga amarela tipo codium viscosoCladostephus spongiosus (codium que parece que tem areia)Codium adhaerens (tipo nenúfar)Colpomenia sinuosa (alga bolhas)Fucus vesiculosusDictyota dichotoma (alga azul laminada)Dictyota castanhaAlga castanha sp.Alga castanha laminadaAlga castanha filamentosaAlga castanha incrustanteAlga castanha carnudaAlga castanha tufosaAlga verde/azul - lavandaSphacelaria rigidula (pompons castanhos)Cystoseira sp.Laminaria sp.TOTAL2observaçõesColuna1Coluna2
14842017-01-1108:40:000.70Clear sky15.2AC ARASupraRock0000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00NaNNaNNaN
6362013-08-2110:45:000.60Clear sky20AF SFAMediumPuddle/Rock25070.05.20.00.013.00.00.00.03.00.00.00.00.00.00.00.00.00.00.001.00.00.00.00.00.00.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.056.2NaNNaNNaN
11252015-07-1709:45:000.90Cloudy18AF AR SFDMediumRock2250.00.60.00.05.00.00.00.00.00.00.00.00.50.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.033.00.00.00.00.04.00.00.00.00.06.00.00.013.013.000.00.00.00.00.00.00.00.00.00.00.00.03.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.10.00.00.00.00.00.00.00.00.00.00.000.088.2NaNNaNNaN
10182015-02-2311:45:000.80Clear sky12AF SFASupraSand/Pebble0000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00NaNNaNNaN
14672016-12-1609:40:000.50Rain15AC ARDSupraRock52000.00.10.012.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.064.1NaNNaNNaN
16682017-09-0809:00:000.66Clear sky17.9AC AF IREMediumRock0000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.010.01.00.00.00.00.00.00.00.023.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.56.00.00.00.00.00.00.00.00.00.00.00.00.04.00.01.00.00.00.00.00.00.00.00.00.00.050.000.095.5NaNNaNNaN
18282018-05-2909:00:000.80Cloudy19AF SFFSupraRock0000.00.00.02.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.02000.0
18552019-01-2509:00:000.90Sunny15AF SFASupraRock0000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00000.0
8982014-09-0910:15:000.40Clear sky22AF ARAMediumRock/Sand0000.00.00.00.00.00.00.00.00.00.00.00.00.10.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.055.04.00.00.00.00.00.00.00.00.00.00.00.01.00.000.00.00.00.00.00.00.20.00.00.00.00.00.15.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.065.4NaNNaNNaN
15542017-04-1309:30:000.83Clear sky17.4AC AR IRBSupraSand0000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00NaNNaNNaN
DateHourTideWeather ConditionWater temperature (ºC)SamplerZoneSupratidal/Middle IntertidalSubstrateChthamalus sp.Balanus perforatusPatella sp.Siphonaria algesiraeGibbula sp.Monodonta lineataLittorina neritoidesMytillus sp.Nassariidae (búzio)Chiton sp.Pollicipes pollicipesActinia equinaAnemonia sulcataOphiothrix sp.Paracentrotus lividusHymeniacidon sanguineaAglaophenia pluma (hidrozoário branco ramificado)Alga branca ramificadaSabellaria alveolata (tubos)CerianthidaeAnémona N.I.Actinia fragaceaGymnangium montaguiAnémona branca com estrias (fam. Cerianthidae)Actinothoe sphyrodeta (anémona branca e laranja)Oncidiella celtica (lesma)Calliostoma sp. (burrié bicudo)Polysyncraton sp. (ascídias vermelhas)Didemnum sp. (ascídeas brancas)Burrié negro N.I.Búzio/burrié n.i.Watersipora subtorquata (Ascídea)Verrucaria mauraLichina pygmaea (líquene folhoso)Coralina elongataLithophillum incrustansLitophylum lichenoidesLitophylum tortuosumMesophylum lichenoidesGelidium sp. (tipo raquete)Asparagopsis armata (tufosa)Asparagopsis armata (adulta)Porphyra sp.Ceramium (forma taças)Mastocarpus sp. (alga preta só mediolitoral)Gigartina sp. (língua do diabo)Plocamium sp.Caulacanthus sp.CondracanthusAlga vermelha filamentosaAlga vermelha vesicularAlga vermelha laminadaAlga vermelha carnudaHildenbrandia sp. (incrustante vermelha)Chondria coerulescens (alga verde/azul filamentosa)Alga vermelha "encaracolada" (que parece ceramium pequeno)Alga vermelha semelhante a Ulva rigidaPorphiraOsmundea pinnatifidaAlga vermelha ramificada (Polysiphonia?)Ahnfeltiopsis devoniensisStypocaulon scoparium (alga verde tufosa que parece pinheiro)Ulva rigidaCladophora sp. (limo)Limo N.I.Ulva intestinalisUlva sp.Codium sp. (alga verde carnuda)Alga verde carnuda ramificadaNemoderma sp.(alga tipo musgo)alga verde tufosaalga verde (tipo couve flor)Alga verde incrustanteAlga verde n.i.Alga verde filamentosaAlga amarela tipo codium viscosoCladostephus spongiosus (codium que parece que tem areia)Codium adhaerens (tipo nenúfar)Colpomenia sinuosa (alga bolhas)Fucus vesiculosusDictyota dichotoma (alga azul laminada)Dictyota castanhaAlga castanha sp.Alga castanha laminadaAlga castanha filamentosaAlga castanha incrustanteAlga castanha carnudaAlga castanha tufosaAlga verde/azul - lavandaSphacelaria rigidula (pompons castanhos)Cystoseira sp.Laminaria sp.TOTAL2observaçõesColuna1Coluna2
1672012-03-2209:00:000.80Clear sky17AF SFBMediumRock/Canal40.30.750.30.20.00.02.00.20.00.00.00.00.00.00.50.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.031.00.00.00.00.00.30.00.00.00.01.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.011.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.051.55NaNNaNNaN
2112012-04-2410:00:001.00Cloudy16.5AF SFBMediumRock/Sand0000.00.00.00.00.00.00.00.00.00.00.00.00.10.00.00.00.00.00.000.00.00.00.00.00.00.20.0NaN0.00.085.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.02.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02.00.00.00.00.00.000.089.3NaNNaNNaN
14862017-01-1310:00:000.67Cloudy15AC AR IRDMediumRock20.52.50.00.00.00.00.00.00.00.00.00.00.00.00.50.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.035.00.00.00.00.00.00.00.00.00.00.00.00.057.00.500.00.00.00.00.00.00.00.00.00.00.00.02.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.0100NaNNaNNaN
12272015-12-1509:00:000.80Clear sky16AF SFASupraPebble0000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00NaNNaNNaN
10142015-02-2309:30:000.80Clear sky12AF SFBMediumRock/Sand0000.00.00.00.00.00.00.00.00.00.00.00.02.00.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.040.01.00.00.00.00.00.00.00.00.00.01.00.05.00.000.00.00.00.00.00.00.00.00.00.00.00.00.10.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.049.1NaNNaNNaN
15702017-05-2408:00:000.64Clear sky17.8AC AR IREMediumRock/Sand/Sea0000.00.00.00.00.00.00.00.00.00.00.00.06.00.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.020.02.00.00.00.00.02.024.00.016.00.00.00.00.00.000.00.00.00.50.00.00.00.00.00.00.00.05.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.020.077.5NaNNaNNaN
7542014-02-1411:45:000.90Rain13AF SFESupraSand0000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00NaNNaNNaN
5512013-04-1212:30:000.80Clear sky16AF SFDSupraRock7000.21.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.013.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.021.2NaNNaNNaN
18182018-05-2809:00:000.80Cloudy19AF SFDSupraPebble0000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00000.0
1122012-02-2209:30:000.70Clear sky16AR SFBMediumRock471.55.50.10.00.00.011.00.00.00.01.50.00.00.00.10.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.05.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.02.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.03.00.00.00.00.00.000.076.7NaNNaNNaN

Duplicate rows

Most frequently occurring

DateTideWeather ConditionSamplerZoneSupratidal/Middle IntertidalSubstrateSiphonaria algesiraeGibbula sp.Monodonta lineataLittorina neritoidesMytillus sp.Nassariidae (búzio)Chiton sp.Pollicipes pollicipesActinia equinaAnemonia sulcataOphiothrix sp.Paracentrotus lividusHymeniacidon sanguineaAglaophenia pluma (hidrozoário branco ramificado)Alga branca ramificadaSabellaria alveolata (tubos)CerianthidaeAnémona N.I.Actinia fragaceaGymnangium montaguiAnémona branca com estrias (fam. Cerianthidae)Actinothoe sphyrodeta (anémona branca e laranja)Oncidiella celtica (lesma)Calliostoma sp. (burrié bicudo)Polysyncraton sp. (ascídias vermelhas)Didemnum sp. (ascídeas brancas)Burrié negro N.I.Búzio/burrié n.i.Watersipora subtorquata (Ascídea)Verrucaria mauraLichina pygmaea (líquene folhoso)Coralina elongataLithophillum incrustansLitophylum lichenoidesLitophylum tortuosumMesophylum lichenoidesGelidium sp. (tipo raquete)Asparagopsis armata (tufosa)Asparagopsis armata (adulta)Porphyra sp.Ceramium (forma taças)Mastocarpus sp. (alga preta só mediolitoral)Gigartina sp. (língua do diabo)Plocamium sp.Caulacanthus sp.CondracanthusAlga vermelha filamentosaAlga vermelha vesicularAlga vermelha laminadaAlga vermelha carnudaHildenbrandia sp. (incrustante vermelha)Chondria coerulescens (alga verde/azul filamentosa)Alga vermelha "encaracolada" (que parece ceramium pequeno)Alga vermelha semelhante a Ulva rigidaPorphiraOsmundea pinnatifidaAlga vermelha ramificada (Polysiphonia?)Ahnfeltiopsis devoniensisStypocaulon scoparium (alga verde tufosa que parece pinheiro)Ulva rigidaCladophora sp. (limo)Limo N.I.Ulva intestinalisUlva sp.Codium sp. (alga verde carnuda)Alga verde carnuda ramificadaNemoderma sp.(alga tipo musgo)alga verde tufosaalga verde (tipo couve flor)Alga verde incrustanteAlga verde n.i.Alga verde filamentosaAlga amarela tipo codium viscosoCladostephus spongiosus (codium que parece que tem areia)Codium adhaerens (tipo nenúfar)Colpomenia sinuosa (alga bolhas)Fucus vesiculosusDictyota dichotoma (alga azul laminada)Dictyota castanhaAlga castanha sp.Alga castanha laminadaAlga castanha filamentosaAlga castanha incrustanteAlga castanha carnudaAlga castanha tufosaAlga verde/azul - lavandaSphacelaria rigidula (pompons castanhos)Laminaria sp.TOTAL2Coluna1Coluna2# duplicates
02014-11-121.2Clear skyAF SFESupraRock/Sand0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN2